Here , we provide a thorough introduction and investigation of the Combinatorial Multi-Objective Evolutionary Algorithm ( CMOEA ) , which avoids ordering subtasks by allowing all combinations of subtasks to be explored simultaneously . We compare CMOEA against two algorithms that can similarly optimize on multiple subtasks simultaneously : NSGA-II and Lexicase Selection . The algorithms are tested on a multimodal robotics problem with six subtasks as well as a maze navigation problem with a hundred subtasks . We call this algorithm the hypervolume-based MORL algorithm or HB-MORL and conduct an empirical study of the performance of the algorithm using multiple quality assessment metrics from multi-objective optimization . We compare the hypervolume-based learning algorithm on different environments to two multi-objective algorithms that rely on scalarization techniques , such as the linear scalarization and the weighted Chebyshev function . We conclude that HB-MORL significantly outperforms the linear scalarization method and performs similarly to the Chebyshev algorithm without requiring any user-specified emphasis on particular objectives . The proposed technique is tested on the 33-bus ADS considering different planning schemes to provide the most suitable planning scheme to the ADS planners . Moreover , the accuracy of the proposed algorithm is confirmed by comparing it with other multi-objective algorithms . Real-time traffic control is very important for urban transportation systems . Due to conflicts among different optimization objectives , the existing multi-objective models often convert into single-objective problems through weighted sum method . To obtain real-time signal parameters and evaluation indices , this article puts forward a Pareto front – based multi-objective traffic signal control model using particle swarm optimization algorithm . In the first proposed approach , modified and operational features of the scheduling environment are added to the terminal set , and a multigenic system is used , whereas in the second approach , priority rules are used as automatically defined functions , which are combined with the cellular system for gene expression programming . A comparison shows that the second approach generates better results than the first ; however , all of the extracted rules yield better results than the rules from the literature , especially for the defined multi-objective function consisting of makespan , mean lateness and mean flow time . The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems . However , the relaxation may bring in sensitive weighted parameters and additional calculation error . In this paper , we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation . We transform sparse unmixing to a multi-objective optimization problem , which contains two correlative objectives : minimizing the reconstruction error and controlling the endmember sparsity . Sparse unmixing is actually l 0 problem which is NP-h ard , and a relaxation is often used . In this paper , we attempt to deal with l 0 problem directly via a multi-objective based method , which is a non-convex manner . The characteristics of hyperspectral images are integrated into the proposed method , which leads to a new spectra and multi-objective based sparse unmixing method ( SMoSU ) . This paper proposes an optimization scheme with regard to camping along the Colorado River , relevant data comes from MCM(The mathematical contest in modeling)2012[1 ] , problem B. With purpose of precise formulation for the drifting-tour on the Colorado River[2 ] , a multi-objective optimizing model is established with consideration of 4 factors . In particular , an intelligent match-search algorithm is designed to figure out the model solution . Then , we have evaluated the performances of the proposed SaCIDE-<inline-formula > < tex-math notation="LaTeX">$r$ < /tex-math></inline-formula > approach by studying some numerical optimization problems of Congress on Evolutionary Computation ( CEC ) 2013 with < inline-formula > < tex-math notation="LaTeX">$D = 30 $ < /tex-math></inline-formula > , compared with seven state-of-the-art DE algorithms . Moreover , the proposed SaCIDE-<inline-formula > < tex-math notation="LaTeX">$r$ < /tex-math></inline-formula > method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods , resulting in very competitive performances . The comprehensive experimental results have demonstrated that the presented SaCIDE-<inline-formula > < tex-math notation="LaTeX">$r$ < /tex-math></inline-formula > method might be a feasible solution for such a kind of optimization problem . This is due to the lack of a relationship which connecting remote sensing data with land surface parameters . The objective of this paper is to develop a systemic method to retrieve a number of land surface parameters through a combination of microwave remote sensing , radiative transfer modeling , land surface modeling and multi-objective optimizing . The microwave brightness temperature observations are used as the calibration references . However , these studies were conducted applying only the combined strategy . MethodsIn this paper , we present experimental results comparing both strategies with three different approaches : the traditional one , a simple GA-based , and a multi-objective one . ResultsThe results show better performance of the multi-objective approach independently of the strategy adopted . Purpose The optimization of investment portfolios is a topic of major importance in financial decision making , with many relevant models available in the relevant literature . The purpose of this paper is to perform a thorough comparative assessment of different bi-objective models as well as multi-objective one , in terms of the performance and robustness of the whole set of Pareto optimal portfolios . Design/methodology/approach In this study , three bi-objective models are considered ( mean-variance ( MV ) , mean absolute deviation , conditional value-at-risk ( CVaR ) ) , as well as a multi-objective model . Abstract This work describes an efficient model-based procedure for computing seasonal sub-optimal irrigation schedules . The optimization problem is formulated as a multi-objective one , with the objective function consisting of the end-of-season yield and total irrigation . In order to compute the sub-optimal irrigation schedules , a hybrid formulation is presented , according to which a single irrigation event is optimized for the five subsequent days , while irrigation during the rest of the season is assumed to be triggered at some soil water levels which are determined as part of the optimization . The findings revealed that the MEHSMODE-ESNN method statistically outperformed all the other methods using the different data sets and evaluation criteria . It is concluded that multi objective proposed methods have been evinced as the best proposed methods for most of the data sets used in this study . The findings have proven that the proposed algorithms attained the optimal presynaptic neurons and parameters values and MOO approach was applicable for the ESNN . Also trapezoidal membership functions are applied to each objective function and constraints to describe a each fuzzy goal . A numerical example is provided to illustrate the efficiency of the multi-objective proposed approach . The multi-objective function is expressed to minimize the total power loss , total operating cost , and improve the voltage stability index of the radial distribution system ( RDS ) . Multi-objective proposed algorithms are used to generate the Pareto optimal solutions ; and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution . The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems . Approximation-Guided Evolution ( AGE ) [ 4 ] is a recently presented multi-objective algorithm that outperforms state-of-the-art multi-multi-objective algorithms in terms of approximation quality . This holds for problems with many objectives , but AGE 's performance is not competitive on problems with few objectives . There are still rooms for improvement the algorithm performance by manipulating the algorithm strategy . This thesis presents two variants of multi-objective type algorithms based on a Spiral Dynamic Algorithm ( SDA ) with application to optimize a Proportional-Derivative ( PD ) controller for an Inverted Pendulum System . The first variant is known as a Nondominated Sorting Multi-objective Spiral Dynamic Algorithm ( MOSDA-NS ) . To obtain best compromise solutions for each agent , integer linear programming model and greedy heuristics based on \(\varepsilon \)-constraint approach are proposed to compute exact and approximate Pareto fronts . A Non-dominated Sorting Genetic Algorithm ( NSGA-II ) is developed to generate Pareto front . Experimental results are driven to analyse the performances of the proposed methods . The ANN 's inputs include weather and occupancy predictions as well as taking account of the buildings thermal inertia to predict energy consumption , predicted percentage dissatisfied , PPD , and indoor temperature . A multi-objective GA uses the ANN to calculate the energy sum over 24 hours and the average occupied PPD as its two objectives . The optimisation strategy was applied on a week in January over which it reduced energy consumption by 4.93 % and improved PPD by 0.76 % . Level 1 is for the process of forecasting time series cost data , while level 2 evaluates the forecasting . Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model . Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting . We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms ( GA ) ; the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time . We compare the proposed multi-objective GA based approach with two other approaches , namely : 1 ) CURE-based approach , which is known as one of the most efficient clustering algorithms ; 2 ) Chien et al. clustering approach , which is an automatic interval partition method based on variation of density . Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al. ’s work in terms of runtime , number of large itemsets and number of association rules . Unlike conventional local search , the proposed adaptive local search scheme automatically determines whether the VNS is used in a GA loop or not . We investigate the performance of the proposed approach in comparison to multi-objective GA-based approaches without local search and augmented with traditional local search . The computational results indicate that the proposed approach with adaptive VNS is more efficient in most of the performance measures and can find near-optimal layouts by optimizing multiple criteria simultaneously . We propose an automated clustering method based on multiobjective genetic algorithms ( GA ) ; the aim of this method is to automatically cluster values of a given quantitative attribute to obtain large number of large itemsets in low duration ( time ) . We compare the proposed multi-objective GA-based approach with CURE-based approach . In addition to the autonomous specification of fuzzy sets , experimental results showed that the proposed automated clustering exhibits good performance over CURE-based approach in terms of runtime as well as the number of large itemsets and interesting association rules . However , the most appropriate objective functions to be used with each other are still under debate since many similar objective functions have been proposed over the years . We show how those objectives correlate , investigate their performance when they are used in both the single- and multi-objective GA , and determine the community structure properties they tend to produce . However , the absence of sparseness in the Lagrange multiplier vector ( i.e. the solution ) is a significant problem for the effective use of these classifiers . In order to overcome this lack of sparseness , we propose both single and multi-objective GA approaches to leave a few support vectors out of the solution without affecting the classifier 's accuracy and even improving it . The main idea is to leave out outliers , non-relevant patterns or those ones which can be corrupted with noise and thus prevent classifiers to achieve higher accuracies along with a reduced set of support vectors . However , too much more attention was focus on a circuit 's function and many other factors had been neglected which caused the circuit had little applicability . This paper proposes an automated design approach for analog circuit based on a multi-objective adaptive GA . The multi-objective fitness evaluation method , which can dynamic adjust parameter , is selected . Welding simulation experiments were conducted on a typical widely used mounting bracket which has eight seams . Multi-objective based GA effectively reduces the computational complexity over exhaustive search with significant reduction of both structural deformation ( ~80 % ) and residual stress ( ~15 % ) The developed method accelerates the generation of optimum schedules.,Unlike methods reported in the literature , the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM . This method considers uncertainties of productivity rates , quantities and availability of resources while utilizing multi-objective GA function to minimize project duration , cost and work interruptions simultaneously . Schedule buffers are assigned whether optimized schedule allows for interruptions or not . In this article an improved genetic algorithm is proposed to solve the problem . The multi-objective optimization model was compared with independent maintenance model to verify its superiority , and the multi-objective improved GA can find more Pareto solutions for the decision maker . Finally the example calculation shows that the integrated optimization can find the solution with shorter completion time , eliminate waste and improve production efficiency . The considered problem minimizes environmental emission and non-smooth fuel cost simultaneously while fulfilling the system operating constraints . The proposed MORCGA technique evolves a multi-objective version of GA by proposing redefinition of global best and local best individuals in multi-objective optimization domain . The performance of the proposed MO-RCGA enhanced with biased Initialization , dynamic parameter setting , and elitism is carried out . In this paper , we propose a novel multi-objective formulation that considers : the Root Mean Square Deviation ( RMSD ) difference in the coordinates of ligands and the binding ( intermolecular ) energy , as two objectives to evaluate the quality of the ligand-protein interactions . To determine the kind of Pareto front approximations that can be obtained , we have selected a set of representative multi-objective algorithms such as NSGA-II , SMPSO , GDE3 , and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts . In addition , a comparison with LGA , a reference single-objective evolutionary algorithm for molecular docking ( AutoDock ) is carried out . Scatter density distribution plots of design parameters are also obtained to find their trends , which can give more insight into GSHPs design parameters . The solutions from five different Evolutionary Algorithms ( EAs ) , NSGA-II , GDE3 , IBEA , SMPSO , and SPEA2 , are compared . Three different refrigerants ( R134-a , R123 and isobutene ) are also compared to find the most suitable working fluid for the decision space delineated . Here , the objective is to minimize the roughness of the surface and tool flank wear , and maximize the material removal rate . For this purpose , we have applied five different Multi-objective Evolutionary Algorithms ( MOEAs ) : ( i ) Non-dominated Sorting Genetic Algorithm-II ( NSGA-II ) and ( ii ) Archive-based Steady-state Micro Genetic Algorithm ( ASMiGA ) based on GA , ( iii ) Archive based hybrid Scatter Search ( AbYSS ) based on scatter search , ( iv ) Generalized Differential Evolution 3 ( GDE3 ) based on Differential Evolution ( DE ) and ( v ) Multi-objective Particle Swarm Optimization ( MOPSO ) based on Particle Swarm Optimization ( PSO ) to solve the cutting parameter optimization problem of GFRP . Performances of these algorithms have been evaluated and compared based on three performance metrics : ( i ) Hyper volume ( HV ) , ( ii ) Inverted generalized distance ( IGD ) and ( iii ) Generalized spread ( GS ) . In this work we present and discuss results of applying a multi-objective search-based auto-tuner to optimize for three conflicting criteria : execution time , energy consumption , and resource usage . We examine a method , called RS-GDE3 , to tune HPC codes using the Insieme parallelizing and optimizing compiler . Our results demonstrate that RS-GDE3 offers solutions of superior quality than those provided by a hierarchical and a random search at a fraction of the required time ( 5 % ) or energy ( 8 % ) . A comparison to a state-of-the-art multi-objective optimizer ( NSGA-II ) shows that RS-GDE3 computes solutions of higher quality . Finally , based on the trade-off solutions found by RS-GDE3 , we provide a detailed analysis and several hints on how to improve the design of multi-objective auto-tuners and code optimization . An efficient multiobjective differential evolution algorithm for array synthesis is proposed in this article . By introducing novel strategies in the generalized differential evolution GDE3 , the proposed algorithm can generate a better converged and distributed Pareto front than the standard GDE3 . Five typical benchmark functions are optimized by the proposed improved GDE3 IGDE to verify its effectiveness . Furthermore , the proposed algorithm is applied in two-pattern synthesis problems , including an unequal spaced linear array and a conformal array . However , the authors did not investigate the contribution of this technique and that of the GDE3 algorithm separately . So , in this work , the results obtained by GDE3 equipped with the APM scheme ( denoted here by GDE3 + APM ) are compared with those found by the original GDE3 in order to investigate the advantages and limitations of this constraint handling technique in those problems . The results of the GDE3 + APM are also compared with the most commonly used multi-objective meta-heuristic , namely NSGA-II , in order to comparatively evaluate the quality of the solutions obtained with respect to other algorithms from the literature . The analysis indicates that GDE3 + APM is more efficient than both GDE3 and NSGA-II in most performance metrics used when solving the structural multi-objective optimization problems considered here , suggesting that the GDE3 + APM algorithm is promising in this area , and that the APM technique makes a considerable contribution to its performance . Abstract In this paper , an Immune Generalized Differential Evolution 3 ( Immune GDE3 ) algorithm to solve dynamic multi-objective optimization problems ( DMOPs ) is empirically analyzed . Three main issues of the algorithm are explored : ( 1 ) the general performance of Immune GDE3 in comparison with other well-known algorithms , ( 2 ) its sensitivity to different change severities and frequencies , and ( 3 ) the role of its change reaction mechanism based on an immune response . For such purpose , four performance metrics , three unary and one binary , are computed in a comparison against other state-of-the-art dynamic multi-objective evolutionary algorithms ( DMOEAs ) when solving a novel suite of test problems . A proposal for the adaptation of a binary metric , called Two-set-coverage , to evaluate the performance of DMOEAs is also presented in this paper . The statistically validated results indicate that Immune GDE3 is robust to change frequency and severity variations and can track the environmental change finding a good distribution of solutions . Finally , Immune GDE3 has a very competitive performance solving different types of DMOPs and this good performance is mainly attributed to its change reaction mechanism based on an immune response . Numerical results support such findings , showing that Immune GDE3 obtains good results in all performance metrics , especially in the distribution metrics : Spacing(S ) and Two-set-coverage(C-metric ) . For maintaining good diversity , the concepts of Simulated Binary Crossover ( SBX ) based recombination and Dynamic Crowding Distance ( DCD ) are implemented in GDE3 algorithm . The proposed approach is applied to different sets of classical test problems suggested in the MOEA literature to validate the performance of the I-GDE3 . Later , the proposed approach is implemented to Reactive Power Planning ( RPP ) problem . The objective functions are minimization of combined operating and VAR allocation cost and bus voltage profile improvement . The performance of the proposed approach is tested in standard IEEE 30-bus test systems . The performance of I-GDE3 is compared with respect to multi- objective performance measures namely gamma , spread , minimum spacing and Inverted Generational Distance ( IGD ) . The results show the effectiveness of I-GDE3 and confirm its potential to solve the multi-objective problems . So , this paper proposes the IVF/GDE3 - IVF coupled to GDE3 . We evaluated IVF/GDE3 against a set of test problems and compared with canonical GDE3 results . The results show that IVF/GDE3 outperformed GDE3 in most of the benchmarks . The IVFm contributed to a faster convergence for the host algorithm ( GDE3 ) and also improved its efficacy . Therefore , in this paper , to the best of our knowledge , for the first time OBL is successfully adapted for a MOO metaheuristic by using a single population ( no coevolution ) . The proposed MOO metaheuristic is based on the GDE3 method and it is called Opposition-based GDE3 ( OGDE3 ) . OGDE3 utilizes OBL for opposition-based population initialization and self-adaptive oppositionbased generating jumping . This paper proposed a binary version of Generalized Differential Evolution ( BGDE3 ) for multi-label feature selection based on majority voting of solutions and opposition-based learning ( OBL ) . Experimental results show that the proposed algorithm outperforms the continuous GDE3 for multi-label feature selection . Furthermore , the twoobjective optimal solution front is not known a priori . Therefore , we use a multiobjective EA which produces diverse solution space and monitors convergence ; the EA has been demonstrated to work effectively across complex problems of unknown nature . We tested this approach for designing networks of different sizes and found that the approach scales well with larger networks . Then , a specific experiment investigating the convergence of an arbitrary EA to a Pareto front is presented . This experiment gives a basis for a theorem showing a specific multiobjective EA statistically converges to the Pareto front . We conclude by using this work to justify further exploration into the theoretical foundations of EC-based MOP solution methods . It can be performed to restore electricity for out-of-service areas after a fault has been identified and isolated . This paper presents an application of multiobjective EA for energy restoration in large-scale distribution networks . To improve EA performance for energy restoration , two techniques are employed : ( 1 ) the use of an efficient data structure that produces only feasible configurations , save RAM memory and running time ; ( 2 ) a multiobjective method based on subpopulation tables . Because of the disadvantage of Data Envelopment Analysis(DEA ) only for outputs , this paper develops Multi objective DEA Only for Outputs ( MDEAO ) in which components of output vector are extended in different ratios . Relative efficiencies of Decision Making Units(DMUs ) are discussed . However , fuzzy DEA is still limited to fuzzy input/output data while some inputs and outputs might be affected by various factors of uncertainty and information granularity , meaning that they could be better modeled in terms of fuzzy sets of type-2 . In this paper , we develop a multi-objective DEA model in a setting of type-2 fuzzy modeling to evaluate and select the most appropriate sustainable suppliers . In the proposed model , both efficiency and effectiveness are considered to describe the integrated productivity of suppliers . This paper presents a hybrid multi-objective version of EO ( HMEO ) to solve multi-objective combinatorial problems . This new approach consists of a multi-objective EO framework , for the coarse-grain search , which contains a novel multi-objective combinatorial local search framework for the fine-grain search . The chosen problems to test the proposed method are the multi-objective knapsack problem and the multi-objective quadratic assignment problem . In this EO version , additional fitness function is used in stochastic selection of next solution state based on computation and communication assessment of task migration targets . In the multi-objective EO approach we jointly control three objectives relevant in processor load balancing for distributed applications . They are : computational load balance in execution of distributed applications , volume of communication between tasks on different processors and task migration parameters which fight imbalance of processor loads . Evolutionary Algorithms ( EAs ) are particularly suited for solving multi-objective optimization problems . In this paper , we propose an improved version of the Evolutionary Concept Learner ( ECL ) system , called Multi-Objective ECL ( MOECL ) . While the search performed by ECL is based on a single-objective EA , MOECL adopts a multi-objective search strategy . For both problem formulations , two wind test cases are considered for both a square and a rectangular field : a constant wind speed from a predominant wind direction , and a multidirectional test case with three wind speeds and a defined probability of occurrence for each . The exploration of these resulting layouts indicates the value of the advanced multi-objective EPS and the hybrid EPS/GA , and gives insight into how to approach optimizing both completed wind farms and sub-farm stages . The behavior exhibited in these tests cases suggests strategies that can be employed by wind farm developers to facilitate predictable , optimal performance of multi-stage wind farms throughout their useful life . To reduce the computational cost , a variant of k-d tree is used in our approach to preserve all nondominated solutions . Our experiments demonstrate that the algorithm outperforms the other popular multiobjective EA 's , especially for the higher dimensional cases . In order to handling the uncertainty in the optimization problem , we first give a thorough analysis of three important issues on robust optimization . Then , a novel EA called multiobjective optimization EA with robustness enhancement is developed , where the seamless integration of robustness and optimality is achieved by a proposed novel archive updating mechanism applied on the evolutionary process as well as the new robust optimal front building strategy designed to construct the final robust optimal front . Furthermore , the new designed archive updating mechanism makes the robust optimization process free of the enormous computational workload induced from sampling . The performance of the single-objective and multi-objective evolutionary algorithms has been statistically analyzed , and the results are reported . The results reveal that among the four single-objective EA techniques , SADE offers better performance due to its inherit self-adaptive capability . Also , during multi-objective optimization , MNSGA-II has provided better solution due the presence of dynamic crowding distance ( DCD ) and control elitism ( CE ) strategies . In this work , we investigate a largely underexplored issue : the approximation performance of EAs in terms of how close the obtained solution is to an optimal solution . We study an EA framework named simple EA with isolated population ( SEIP ) that can be implemented as a single- or multi-objective EA . We present general approximation results of SEIP , and specifically on the minimum set cover problem , we find that SEIP achieves the currently best-achievable approximation ratio . Two different CE approaches were studied , namely Hall of Fame and K-Random Opponents resulting in two new SPEA2 algorithms using CE , which are the SPEA2-CE-HOF and SPEA2-CE-KR algorithms respectively . Using the CC approach , one new SPEA2 algorithm was proposed , which is the SPEA2-CC algorithm . The proposed algorithms were benchmarked against the original SPEA2 using seven scalable benchmark test problems having 3 to 5 objectives based on the generational distance , spacing and coverage metrics . In this paper , we present the application of our fuzzy rule-based classification technique with genetically op- timized accuracy-transparency/interpretability trade-off to the classification of selected protein sequence data coming from the Universal Protein Resource ( UniProt ) repository . Three multi- objective evolutionary optimization algorithms are used and compared in the framework of our approach , i.e. , the well-known Strength Pareto Evolutionary Algorithm 2 ( SPEA2 ) , Nondominated Sorting Genetic Algorithm II ( NSGA-II ) , and our SPEA2 's generalization ( referred to as SPEA3 ) characterized by a higher spread and a better-balanced distribution of solutions . A comparison with 5 alternative approaches is also performed . Further , the average size of the individuals in a GP population depends on the evolutionary approach employed . We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population ; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity .
Here , we provide a thorough introduction and investigation of the Combinatorial Multi-Objective Evolutionary Algorithm ( CMOEA ) , which avoids ordering subtasks by allowing all combinations of subtasks to be explored simultaneously . We compare CMOEA against two algorithms that can similarly optimize on multiple subtasks simultaneously : NSGA-II and Lexicase Selection . The algorithms are tested on a multimodal robotics problem with six subtasks as well as a maze navigation problem with a hundred subtasks .
We call this algorithm the hypervolume-based MORL algorithm or HB-MORL and conduct an empirical study of the performance of the algorithm using multiple quality assessment metrics from multi-objective optimization . We compare the hypervolume-based learning algorithm on different environments to two multi-objective algorithms that rely on scalarization techniques , such as the linear scalarization and the weighted Chebyshev function . We conclude that HB-MORL significantly outperforms the linear scalarization method and performs similarly to the Chebyshev algorithm without requiring any user-specified emphasis on particular objectives .
The proposed technique is tested on the 33-bus ADS considering different planning schemes to provide the most suitable planning scheme to the ADS planners . Moreover , the accuracy of the proposed algorithm is confirmed by comparing it with other multi-objective algorithms .
Real-time traffic control is very important for urban transportation systems . Due to conflicts among different optimization objectives , the existing multi-objective models often convert into single-objective problems through weighted sum method . To obtain real-time signal parameters and evaluation indices , this article puts forward a Pareto front – based multi-objective traffic signal control model using particle swarm optimization algorithm .
In the first proposed approach , modified and operational features of the scheduling environment are added to the terminal set , and a multigenic system is used , whereas in the second approach , priority rules are used as automatically defined functions , which are combined with the cellular system for gene expression programming . A comparison shows that the second approach generates better results than the first ; however , all of the extracted rules yield better results than the rules from the literature , especially for the defined multi-objective function consisting of makespan , mean lateness and mean flow time . The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems .
However , the relaxation may bring in sensitive weighted parameters and additional calculation error . In this paper , we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation . We transform sparse unmixing to a multi-objective optimization problem , which contains two correlative objectives : minimizing the reconstruction error and controlling the endmember sparsity .
Sparse unmixing is actually l 0 problem which is NP-h ard , and a relaxation is often used . In this paper , we attempt to deal with l 0 problem directly via a multi-objective based method , which is a non-convex manner . The characteristics of hyperspectral images are integrated into the proposed method , which leads to a new spectra and multi-objective based sparse unmixing method ( SMoSU ) .
This paper proposes an optimization scheme with regard to camping along the Colorado River , relevant data comes from MCM(The mathematical contest in modeling)2012[1 ] , problem B. With purpose of precise formulation for the drifting-tour on the Colorado River[2 ] , a multi-objective optimizing model is established with consideration of 4 factors . In particular , an intelligent match-search algorithm is designed to figure out the model solution .
Then , we have evaluated the performances of the proposed SaCIDE-<inline-formula > < tex-math notation="LaTeX">$r$ < /tex-math></inline-formula > approach by studying some numerical optimization problems of Congress on Evolutionary Computation ( CEC ) 2013 with < inline-formula > < tex-math notation="LaTeX">$D = 30 $ < /tex-math></inline-formula > , compared with seven state-of-the-art DE algorithms . Moreover , the proposed SaCIDE-<inline-formula > < tex-math notation="LaTeX">$r$ < /tex-math></inline-formula > method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods , resulting in very competitive performances . The comprehensive experimental results have demonstrated that the presented SaCIDE-<inline-formula > < tex-math notation="LaTeX">$r$ < /tex-math></inline-formula > method might be a feasible solution for such a kind of optimization problem .
This is due to the lack of a relationship which connecting remote sensing data with land surface parameters . The objective of this paper is to develop a systemic method to retrieve a number of land surface parameters through a combination of microwave remote sensing , radiative transfer modeling , land surface modeling and multi-objective optimizing . The microwave brightness temperature observations are used as the calibration references .
However , these studies were conducted applying only the combined strategy . MethodsIn this paper , we present experimental results comparing both strategies with three different approaches : the traditional one , a simple GA-based , and a multi-objective one . ResultsThe results show better performance of the multi-objective approach independently of the strategy adopted .
Purpose The optimization of investment portfolios is a topic of major importance in financial decision making , with many relevant models available in the relevant literature . The purpose of this paper is to perform a thorough comparative assessment of different bi-objective models as well as multi-objective one , in terms of the performance and robustness of the whole set of Pareto optimal portfolios . Design/methodology/approach In this study , three bi-objective models are considered ( mean-variance ( MV ) , mean absolute deviation , conditional value-at-risk ( CVaR ) ) , as well as a multi-objective model .
Abstract This work describes an efficient model-based procedure for computing seasonal sub-optimal irrigation schedules . The optimization problem is formulated as a multi-objective one , with the objective function consisting of the end-of-season yield and total irrigation . In order to compute the sub-optimal irrigation schedules , a hybrid formulation is presented , according to which a single irrigation event is optimized for the five subsequent days , while irrigation during the rest of the season is assumed to be triggered at some soil water levels which are determined as part of the optimization .
The findings revealed that the MEHSMODE-ESNN method statistically outperformed all the other methods using the different data sets and evaluation criteria . It is concluded that multi objective proposed methods have been evinced as the best proposed methods for most of the data sets used in this study . The findings have proven that the proposed algorithms attained the optimal presynaptic neurons and parameters values and MOO approach was applicable for the ESNN .
Also trapezoidal membership functions are applied to each objective function and constraints to describe a each fuzzy goal . A numerical example is provided to illustrate the efficiency of the multi-objective proposed approach .
The multi-objective function is expressed to minimize the total power loss , total operating cost , and improve the voltage stability index of the radial distribution system ( RDS ) . Multi-objective proposed algorithms are used to generate the Pareto optimal solutions ; and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution . The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems .
Approximation-Guided Evolution ( AGE ) [ 4 ] is a recently presented multi-objective algorithm that outperforms state-of-the-art multi-multi-objective algorithms in terms of approximation quality . This holds for problems with many objectives , but AGE 's performance is not competitive on problems with few objectives .
There are still rooms for improvement the algorithm performance by manipulating the algorithm strategy . This thesis presents two variants of multi-objective type algorithms based on a Spiral Dynamic Algorithm ( SDA ) with application to optimize a Proportional-Derivative ( PD ) controller for an Inverted Pendulum System . The first variant is known as a Nondominated Sorting Multi-objective Spiral Dynamic Algorithm ( MOSDA-NS ) .
To obtain best compromise solutions for each agent , integer linear programming model and greedy heuristics based on \(\varepsilon \)-constraint approach are proposed to compute exact and approximate Pareto fronts . A Non-dominated Sorting Genetic Algorithm ( NSGA-II ) is developed to generate Pareto front . Experimental results are driven to analyse the performances of the proposed methods .
The ANN 's inputs include weather and occupancy predictions as well as taking account of the buildings thermal inertia to predict energy consumption , predicted percentage dissatisfied , PPD , and indoor temperature . A multi-objective GA uses the ANN to calculate the energy sum over 24 hours and the average occupied PPD as its two objectives . The optimisation strategy was applied on a week in January over which it reduced energy consumption by 4.93 % and improved PPD by 0.76 % .
Level 1 is for the process of forecasting time series cost data , while level 2 evaluates the forecasting . Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model . Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting .
We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms ( GA ) ; the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time . We compare the proposed multi-objective GA based approach with two other approaches , namely : 1 ) CURE-based approach , which is known as one of the most efficient clustering algorithms ; 2 ) Chien et al. clustering approach , which is an automatic interval partition method based on variation of density . Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al. ’s work in terms of runtime , number of large itemsets and number of association rules .
Unlike conventional local search , the proposed adaptive local search scheme automatically determines whether the VNS is used in a GA loop or not . We investigate the performance of the proposed approach in comparison to multi-objective GA-based approaches without local search and augmented with traditional local search . The computational results indicate that the proposed approach with adaptive VNS is more efficient in most of the performance measures and can find near-optimal layouts by optimizing multiple criteria simultaneously .
We propose an automated clustering method based on multiobjective genetic algorithms ( GA ) ; the aim of this method is to automatically cluster values of a given quantitative attribute to obtain large number of large itemsets in low duration ( time ) . We compare the proposed multi-objective GA-based approach with CURE-based approach . In addition to the autonomous specification of fuzzy sets , experimental results showed that the proposed automated clustering exhibits good performance over CURE-based approach in terms of runtime as well as the number of large itemsets and interesting association rules .
However , the most appropriate objective functions to be used with each other are still under debate since many similar objective functions have been proposed over the years . We show how those objectives correlate , investigate their performance when they are used in both the single- and multi-objective GA , and determine the community structure properties they tend to produce .
However , the absence of sparseness in the Lagrange multiplier vector ( i.e. the solution ) is a significant problem for the effective use of these classifiers . In order to overcome this lack of sparseness , we propose both single and multi-objective GA approaches to leave a few support vectors out of the solution without affecting the classifier 's accuracy and even improving it . The main idea is to leave out outliers , non-relevant patterns or those ones which can be corrupted with noise and thus prevent classifiers to achieve higher accuracies along with a reduced set of support vectors .
However , too much more attention was focus on a circuit 's function and many other factors had been neglected which caused the circuit had little applicability . This paper proposes an automated design approach for analog circuit based on a multi-objective adaptive GA . The multi-objective fitness evaluation method , which can dynamic adjust parameter , is selected .
Welding simulation experiments were conducted on a typical widely used mounting bracket which has eight seams . Multi-objective based GA effectively reduces the computational complexity over exhaustive search with significant reduction of both structural deformation ( ~80 % ) and residual stress ( ~15 % )
The developed method accelerates the generation of optimum schedules.,Unlike methods reported in the literature , the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM . This method considers uncertainties of productivity rates , quantities and availability of resources while utilizing multi-objective GA function to minimize project duration , cost and work interruptions simultaneously . Schedule buffers are assigned whether optimized schedule allows for interruptions or not .
In this article an improved genetic algorithm is proposed to solve the problem . The multi-objective optimization model was compared with independent maintenance model to verify its superiority , and the multi-objective improved GA can find more Pareto solutions for the decision maker . Finally the example calculation shows that the integrated optimization can find the solution with shorter completion time , eliminate waste and improve production efficiency .
The considered problem minimizes environmental emission and non-smooth fuel cost simultaneously while fulfilling the system operating constraints . The proposed MORCGA technique evolves a multi-objective version of GA by proposing redefinition of global best and local best individuals in multi-objective optimization domain . The performance of the proposed MO-RCGA enhanced with biased Initialization , dynamic parameter setting , and elitism is carried out .
In this paper , we propose a novel multi-objective formulation that considers : the Root Mean Square Deviation ( RMSD ) difference in the coordinates of ligands and the binding ( intermolecular ) energy , as two objectives to evaluate the quality of the ligand-protein interactions . To determine the kind of Pareto front approximations that can be obtained , we have selected a set of representative multi-objective algorithms such as NSGA-II , SMPSO , GDE3 , and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts . In addition , a comparison with LGA , a reference single-objective evolutionary algorithm for molecular docking ( AutoDock ) is carried out .
Scatter density distribution plots of design parameters are also obtained to find their trends , which can give more insight into GSHPs design parameters . The solutions from five different Evolutionary Algorithms ( EAs ) , NSGA-II , GDE3 , IBEA , SMPSO , and SPEA2 , are compared . Three different refrigerants ( R134-a , R123 and isobutene ) are also compared to find the most suitable working fluid for the decision space delineated .
Here , the objective is to minimize the roughness of the surface and tool flank wear , and maximize the material removal rate . For this purpose , we have applied five different Multi-objective Evolutionary Algorithms ( MOEAs ) : ( i ) Non-dominated Sorting Genetic Algorithm-II ( NSGA-II ) and ( ii ) Archive-based Steady-state Micro Genetic Algorithm ( ASMiGA ) based on GA , ( iii ) Archive based hybrid Scatter Search ( AbYSS ) based on scatter search , ( iv ) Generalized Differential Evolution 3 ( GDE3 ) based on Differential Evolution ( DE ) and ( v ) Multi-objective Particle Swarm Optimization ( MOPSO ) based on Particle Swarm Optimization ( PSO ) to solve the cutting parameter optimization problem of GFRP . Performances of these algorithms have been evaluated and compared based on three performance metrics : ( i ) Hyper volume ( HV ) , ( ii ) Inverted generalized distance ( IGD ) and ( iii ) Generalized spread ( GS ) .
In this work we present and discuss results of applying a multi-objective search-based auto-tuner to optimize for three conflicting criteria : execution time , energy consumption , and resource usage . We examine a method , called RS-GDE3 , to tune HPC codes using the Insieme parallelizing and optimizing compiler . Our results demonstrate that RS-GDE3 offers solutions of superior quality than those provided by a hierarchical and a random search at a fraction of the required time ( 5 % ) or energy ( 8 % ) . A comparison to a state-of-the-art multi-objective optimizer ( NSGA-II ) shows that RS-GDE3 computes solutions of higher quality . Finally , based on the trade-off solutions found by RS-GDE3 , we provide a detailed analysis and several hints on how to improve the design of multi-objective auto-tuners and code optimization .
An efficient multiobjective differential evolution algorithm for array synthesis is proposed in this article . By introducing novel strategies in the generalized differential evolution GDE3 , the proposed algorithm can generate a better converged and distributed Pareto front than the standard GDE3 . Five typical benchmark functions are optimized by the proposed improved GDE3 IGDE to verify its effectiveness . Furthermore , the proposed algorithm is applied in two-pattern synthesis problems , including an unequal spaced linear array and a conformal array .
However , the authors did not investigate the contribution of this technique and that of the GDE3 algorithm separately . So , in this work , the results obtained by GDE3 equipped with the APM scheme ( denoted here by GDE3 + APM ) are compared with those found by the original GDE3 in order to investigate the advantages and limitations of this constraint handling technique in those problems . The results of the GDE3 + APM are also compared with the most commonly used multi-objective meta-heuristic , namely NSGA-II , in order to comparatively evaluate the quality of the solutions obtained with respect to other algorithms from the literature . The analysis indicates that GDE3 + APM is more efficient than both GDE3 and NSGA-II in most performance metrics used when solving the structural multi-objective optimization problems considered here , suggesting that the GDE3 + APM algorithm is promising in this area , and that the APM technique makes a considerable contribution to its performance .
Abstract In this paper , an Immune Generalized Differential Evolution 3 ( Immune GDE3 ) algorithm to solve dynamic multi-objective optimization problems ( DMOPs ) is empirically analyzed . Three main issues of the algorithm are explored : ( 1 ) the general performance of Immune GDE3 in comparison with other well-known algorithms , ( 2 ) its sensitivity to different change severities and frequencies , and ( 3 ) the role of its change reaction mechanism based on an immune response . For such purpose , four performance metrics , three unary and one binary , are computed in a comparison against other state-of-the-art dynamic multi-objective evolutionary algorithms ( DMOEAs ) when solving a novel suite of test problems . A proposal for the adaptation of a binary metric , called Two-set-coverage , to evaluate the performance of DMOEAs is also presented in this paper . The statistically validated results indicate that Immune GDE3 is robust to change frequency and severity variations and can track the environmental change finding a good distribution of solutions . Finally , Immune GDE3 has a very competitive performance solving different types of DMOPs and this good performance is mainly attributed to its change reaction mechanism based on an immune response . Numerical results support such findings , showing that Immune GDE3 obtains good results in all performance metrics , especially in the distribution metrics : Spacing(S ) and Two-set-coverage(C-metric ) .
For maintaining good diversity , the concepts of Simulated Binary Crossover ( SBX ) based recombination and Dynamic Crowding Distance ( DCD ) are implemented in GDE3 algorithm . The proposed approach is applied to different sets of classical test problems suggested in the MOEA literature to validate the performance of the I-GDE3 . Later , the proposed approach is implemented to Reactive Power Planning ( RPP ) problem . The objective functions are minimization of combined operating and VAR allocation cost and bus voltage profile improvement . The performance of the proposed approach is tested in standard IEEE 30-bus test systems . The performance of I-GDE3 is compared with respect to multi- objective performance measures namely gamma , spread , minimum spacing and Inverted Generational Distance ( IGD ) . The results show the effectiveness of I-GDE3 and confirm its potential to solve the multi-objective problems .
So , this paper proposes the IVF/GDE3 - IVF coupled to GDE3 . We evaluated IVF/GDE3 against a set of test problems and compared with canonical GDE3 results . The results show that IVF/GDE3 outperformed GDE3 in most of the benchmarks . The IVFm contributed to a faster convergence for the host algorithm ( GDE3 ) and also improved its efficacy .
Therefore , in this paper , to the best of our knowledge , for the first time OBL is successfully adapted for a MOO metaheuristic by using a single population ( no coevolution ) . The proposed MOO metaheuristic is based on the GDE3 method and it is called Opposition-based GDE3 ( OGDE3 ) . OGDE3 utilizes OBL for opposition-based population initialization and self-adaptive oppositionbased generating jumping .
This paper proposed a binary version of Generalized Differential Evolution ( BGDE3 ) for multi-label feature selection based on majority voting of solutions and opposition-based learning ( OBL ) . Experimental results show that the proposed algorithm outperforms the continuous GDE3 for multi-label feature selection .
Furthermore , the twoobjective optimal solution front is not known a priori . Therefore , we use a multiobjective EA which produces diverse solution space and monitors convergence ; the EA has been demonstrated to work effectively across complex problems of unknown nature . We tested this approach for designing networks of different sizes and found that the approach scales well with larger networks .
Then , a specific experiment investigating the convergence of an arbitrary EA to a Pareto front is presented . This experiment gives a basis for a theorem showing a specific multiobjective EA statistically converges to the Pareto front . We conclude by using this work to justify further exploration into the theoretical foundations of EC-based MOP solution methods .
It can be performed to restore electricity for out-of-service areas after a fault has been identified and isolated . This paper presents an application of multiobjective EA for energy restoration in large-scale distribution networks . To improve EA performance for energy restoration , two techniques are employed : ( 1 ) the use of an efficient data structure that produces only feasible configurations , save RAM memory and running time ; ( 2 ) a multiobjective method based on subpopulation tables .
Because of the disadvantage of Data Envelopment Analysis(DEA ) only for outputs , this paper develops Multi objective DEA Only for Outputs ( MDEAO ) in which components of output vector are extended in different ratios . Relative efficiencies of Decision Making Units(DMUs ) are discussed .
However , fuzzy DEA is still limited to fuzzy input/output data while some inputs and outputs might be affected by various factors of uncertainty and information granularity , meaning that they could be better modeled in terms of fuzzy sets of type-2 . In this paper , we develop a multi-objective DEA model in a setting of type-2 fuzzy modeling to evaluate and select the most appropriate sustainable suppliers . In the proposed model , both efficiency and effectiveness are considered to describe the integrated productivity of suppliers .
This paper presents a hybrid multi-objective version of EO ( HMEO ) to solve multi-objective combinatorial problems . This new approach consists of a multi-objective EO framework , for the coarse-grain search , which contains a novel multi-objective combinatorial local search framework for the fine-grain search . The chosen problems to test the proposed method are the multi-objective knapsack problem and the multi-objective quadratic assignment problem .
In this EO version , additional fitness function is used in stochastic selection of next solution state based on computation and communication assessment of task migration targets . In the multi-objective EO approach we jointly control three objectives relevant in processor load balancing for distributed applications . They are : computational load balance in execution of distributed applications , volume of communication between tasks on different processors and task migration parameters which fight imbalance of processor loads .
Evolutionary Algorithms ( EAs ) are particularly suited for solving multi-objective optimization problems . In this paper , we propose an improved version of the Evolutionary Concept Learner ( ECL ) system , called Multi-Objective ECL ( MOECL ) . While the search performed by ECL is based on a single-objective EA , MOECL adopts a multi-objective search strategy .
For both problem formulations , two wind test cases are considered for both a square and a rectangular field : a constant wind speed from a predominant wind direction , and a multidirectional test case with three wind speeds and a defined probability of occurrence for each . The exploration of these resulting layouts indicates the value of the advanced multi-objective EPS and the hybrid EPS/GA , and gives insight into how to approach optimizing both completed wind farms and sub-farm stages . The behavior exhibited in these tests cases suggests strategies that can be employed by wind farm developers to facilitate predictable , optimal performance of multi-stage wind farms throughout their useful life .
To reduce the computational cost , a variant of k-d tree is used in our approach to preserve all nondominated solutions . Our experiments demonstrate that the algorithm outperforms the other popular multiobjective EA 's , especially for the higher dimensional cases .
In order to handling the uncertainty in the optimization problem , we first give a thorough analysis of three important issues on robust optimization . Then , a novel EA called multiobjective optimization EA with robustness enhancement is developed , where the seamless integration of robustness and optimality is achieved by a proposed novel archive updating mechanism applied on the evolutionary process as well as the new robust optimal front building strategy designed to construct the final robust optimal front . Furthermore , the new designed archive updating mechanism makes the robust optimization process free of the enormous computational workload induced from sampling .
The performance of the single-objective and multi-objective evolutionary algorithms has been statistically analyzed , and the results are reported . The results reveal that among the four single-objective EA techniques , SADE offers better performance due to its inherit self-adaptive capability . Also , during multi-objective optimization , MNSGA-II has provided better solution due the presence of dynamic crowding distance ( DCD ) and control elitism ( CE ) strategies .
In this work , we investigate a largely underexplored issue : the approximation performance of EAs in terms of how close the obtained solution is to an optimal solution . We study an EA framework named simple EA with isolated population ( SEIP ) that can be implemented as a single- or multi-objective EA . We present general approximation results of SEIP , and specifically on the minimum set cover problem , we find that SEIP achieves the currently best-achievable approximation ratio .
Two different CE approaches were studied , namely Hall of Fame and K-Random Opponents resulting in two new SPEA2 algorithms using CE , which are the SPEA2-CE-HOF and SPEA2-CE-KR algorithms respectively . Using the CC approach , one new SPEA2 algorithm was proposed , which is the SPEA2-CC algorithm . The proposed algorithms were benchmarked against the original SPEA2 using seven scalable benchmark test problems having 3 to 5 objectives based on the generational distance , spacing and coverage metrics .
In this paper , we present the application of our fuzzy rule-based classification technique with genetically op- timized accuracy-transparency/interpretability trade-off to the classification of selected protein sequence data coming from the Universal Protein Resource ( UniProt ) repository . Three multi- objective evolutionary optimization algorithms are used and compared in the framework of our approach , i.e. , the well-known Strength Pareto Evolutionary Algorithm 2 ( SPEA2 ) , Nondominated Sorting Genetic Algorithm II ( NSGA-II ) , and our SPEA2 's generalization ( referred to as SPEA3 ) characterized by a higher spread and a better-balanced distribution of solutions . A comparison with 5 alternative approaches is also performed .
Further , the average size of the individuals in a GP population depends on the evolutionary approach employed . We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population ; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity .
Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm Hypervolume-Based Multi-Objective Reinforcement Learning Multi-objective optimisation-based active distribution system planning with reconfiguration, intermittent RES, and DSTATCOM Pareto front–based multi-objective real-time traffic signal control model for intersections using particle swarm optimization algorithm Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming Multi-objective based spectral unmixing for hyperspectral images ℓ0-based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation An optimization scheme for camping along Colorado River of crafting-trip Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm Retrieving land surface soil parameters by using passive microwave remote sensing observations and land surface models Evaluating different strategies for integration testing of aspect-oriented programs The robustness of portfolio efficient frontiers Efficient model-based sub-optimal irrigation scheduling using imperfect weather forecasts Multi-objective evolutionary algorithms of spiking neural networks Goal programming approach to fully fuzzy fractional transportation problem A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels A fast approximation-guided evolutionary multi-objective algorithm Multi-objective spiral dynamic algorithms-based for a better accuracy and diversity Fixed Jobs Multi-agent Scheduling Problem with Renewable Resources A smart heating set point scheduler using an artificial neural network and genetic algorithm Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering Genetic Algorithms for Multi-Objective Community Detection in Complex Networks Novel approaches using evolutionary computation for sparse least square support vector machines Automated Design Approach for Analog Circuit Using Genetic Algorithm Deformation and Residual Stress Based Multi-Objective Genetic Algorithm for Welding Sequence Optimization Multi-objective optimization for repetitive scheduling under uncertainty Multi-objective Integrated Optimization Problem of Preventive Maintenance Planning and Flexible Job-Shop Scheduling Multiobjective Real-Coded Genetic Algorithm for Economic/Environmental Dispatch Problem, A New Multi-objective Approach for Molecular Docking Based on RMSD and Binding Energy Multi-objective evolutionary-based optimization of a ground source heat exchanger geometry using various optimization techniques GFRP cutting parameters optimization using different MOEAs Multi-Objective Auto-Tuning with Insieme: Optimization and Trade-Off Analysis for Time, Energy and Resource Usage Synthesis of antenna arrays with an efficient multiobjective differential evolution algorithm Differential evolution with the adaptive penalty method for structural multi-objective optimization Immune Generalized Differential Evolution for dynamic multi-objective environments: An empirical study Application of an Improved Generalized Differential Evolution Algorithm to Multi-objective Optimization Problems IVF/GDE3 - In Vitro Fertilization method coupled to GDE3 OGDE3: Opposition-Based Third Generalized Differential Evolution Opposition-Based Multi-objective Binary Differential Evolution for Multi-label Feature Selection Multicriteria Network Design Using Evolutionary Algorithm Evolutionary Computation and Convergence to a Pareto Front A Multiobjective Evolutionary Algorithm with Node-Depth Encoding for Energy Restoration Multi-objective DEA Only for Outputs & Its Application Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems Multi-objective parallel extremal optimization in processor load balancing for distributed programs A multi-objective Evolutionary Concept Learner A hybrid extended pattern search/genetic algorithm for multi-stage wind farm optimization An efficient evolutionary algorithm for multiobjective optimization problems Evolutionary Multiobjective Optimization With Robustness Enhancement Application of Single- and Multi-Objective Evolutionary Algorithms for Optimal Nonlinear Controller Design in Boiler–Turbine System On the approximation ability of evolutionary optimization with application to minimum set cover Competitive and cooperative coevolution for pareto multiobjective optimization UniProt protein sequence data classification using genetically-optimized fuzzy rule-based systems The influence of mutation on population dynamics in multiobjective genetic programming
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EMO IGARSS GECCO ICE/ITMC ICDM International Conference on Computational Science (4) MICAI (Special Session) AlCoB Euro-Par SEMCCO (1) CEC EMO GECCO ICNC (6) IEEE Congress on Evolutionary Computation GECCO (Companion) IEEE Congress on Evolutionary Computation IJCAI IFSA-SCIS
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