Abstract Artificial bee colony ( ABC ) is a swarm optimization algorithm that shows competitive performance for many optimization problems . However , ABC often suffers from poor exploitation and slow convergence . Unlike many other metaheuristics , it is easy to implement and does not need a lot of parameters . This makes it applicable to a wide range of optimization problems . To evaluate the efficiency of the proposed algorithm , we examined it on the traveling salesman problem ( TSP ) using some benchmark from TSPLIB online library of instances for the TSP . The research concerns the study of neural algorithms for developing CAD tools with A.I. features in VLSI design activities . In this paper the focus is on optimization problems such as partitioning , placement and routing . These problems require massive computational power to be solved ( NP-complete problems ) and the standard approach is usually based on euristic techniques . Based on the analyses of the limitation of a common genetic algorithm and the model of the structure and development of human society , the paper discusses a modified genetic algorithm . The algorithm applies unidirectional inheritance and hierarchical structure to solve the optimizing problems . This method can not only reserve the useful genetic information , but also make the succeeding inheritance more goal-oriented . This work proposes a multi-objective artificial bee colony ( MOABC ) for optimizing problems with multiple objectives . We have adapted the original Artificial Bee Colony ( ABC ) algorithm to multi objective problems with a grid-based approach for maintaining and adaptively assessing the Pareto front . Most of the complex problems which appear in the manufacturing domain and the management of flexible manufacturing systems are optimizing problems . In design and maintenance activities , engineers have to make series of technological and managing decisions at different levels , the purpose of these decisions is to minimize necessary actions or to maximize the benefit . We introduce a novel surrogate-assisted Genetic Algorithm ( GA ) for expensive optimization of problems with discrete categorical variables . Specifically , we leverage the strengths of the Gene-pool Optimal Mixing Evolutionary Algorithm ( GOMEA ) , a state-of-the-art GA , and , for the first time , propose to use a convolutional neural network ( CNN ) as a surrogate model . In this paper , after a beginning on the concept of ant algorithms , a brief survey of the ant-based methods proposed for optimization of problems with continuous design spaces is presented . As a common approach in continuous domains , discretizing the search space is the model presented to be appended to the original ant colony system ( ACS ) algorithm . Genetic algorithm ( GA ) is a useful method for optimization of problems which their calculations have different steps . Also , it is suitable for problems in which some parameters should be assumed at first . We use the energy function where are the edges of a 4 - neighborhood graph and is the regularization parameter . Optimization is performed using belief propagation , with the techniques suggested in . Message passing is performed efficiently using the squared Euclidean distance transform . Additionally , testing requires a careful initialisation for solving a difficult highly non - convex optimization problem , which is slow . The work of , a popular approach for 2.5D reconstruction from a single image , formulates and solves a carefully initialised ( for frontal images only ) non - convex optimization problem for recovering the lighting , depth , and albedo in an alternating manner where each of the sub - problems is a difficult optimization problem per se . In , a quite popular recent approach for creating a neutral subject - specific 2.5D model from a near frontal image , an iterative procedure is proposed which entails localising facial landmarks , face frontalization , solving a photometric stereo problem , local surface normal estimation , and finally shape integration . The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice . Finally , we found that reversing the order of the words in all source sentences ( but not target sentences ) improved the LSTM ’s performance markedly , because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier . section : Introduction Presented in this work is the development of a simulation modelling and solution approach for the scheduling problem of a case study company which can be classified as a project based flow shop . The problem is formulated as a Resource Constrained Project Scheduling Problem ( RCPSP ) and solved with a variable intensity approach . Therefore , we need to deal with large and complex scheduling problems . In this paper , we introduce a model in which several RCPSPs form a whole scheduling problem represented by a directed graph based on a precedence relation . Moreover , resource prices are getting higher recently and we must utilize resources more and more efficiently . This paper extends the scheduling methodology for complex stochastic networks that is based on the solution of a “ fluid ” relaxation ( FR ) at each decision point of the original scheduling problem to stochastic networks with blocking and deadlocking effects . For a clearer and more concrete treatment , the presented results are developed in the operational context of a re-entrant line ( RL ) with finite buffering capacity at each workstation ; these RLs are characterized as “ capacitated RLs ( CRLs ) . The problem is known to be NP-complete and because of this , existing buffer minimisation techniques are heuristics and hence not exact . Modern model-checking tools are quite efficient and they have been successfully applied to scheduling-related problems . We study the feasibility of this approach with examples . Cross-Layer Interactive Framework for Enhancing Network Lifetime and Communication in WSN . Adoption of Cross-Layer based techniques in Wireless Sensor Network ( WSN ) has been reported to be an efficient solution towards improving energy efficiency , scheduling related problems among the resource-limited sensor nodes . The existing approaches are reviewed to ensure that there exists further scope for the improvement and enhancement in terms of resolving the issues associated with even faster interaction among the layers of the conventional protocol stack for catering up and facilitating the demands of faster response time in real-time applications . In this paper , we study a set of real-time scheduling problems whose objectives can be expressed as piecewise linear utility functions . This model has very wide applications in scheduling-related problems , such as mixed criticality , response time minimization , and tardiness analysis . Approximation schemes and matrix vectorization techniques are applied to transform scheduling problems into linear constraint optimization with a piecewise linear and concave objective ; thus , a neural network-based optimization method can be adopted to solve such scheduling problems efficiently . This paper describes the Oz Schedulel ; a workbench for schedulingproblems . Through a graphical interface , the user can freely combine the elements that defne a scheduling strategy . Excluded from consideration in this chapter are paral-lel scheduling models of other typical job characteristics , on-line and randomized algorithms , all of whichare covered in separate chapters of this book . We will address the following aspects of the schedulingproblems : Polynomial solvability and approximability Enumerative algorithms Polynomial-time approximationsAlthough we also consider scheduling jobs of different release dates , we will mainly concentrate onmodels of equal job release dates . The article is devoted to the study of metaheuristic method for scheduling problems solution . The article describes genetic algorithm successfully applied to solve the problems . The special role of scheduling in manage-ment of the problems projects is noted . The review and classification of the approaches of the scheduling problems solution is made . To realize the mention the company must compose production planning hooked on scheduling of production . The scheduling in problems right now , there are excessively long delivery times and the large of total idle time . The Problems cause the height of production cost in company . As to the high request of low power consumption for hardware/software partitioning algorithm in multi-core processor structure , based on reducing the system power consumption , the relevant formal model was defined , and the problem was decomposed into two periods : the task division of heterogeneous multi-core platform and low power scheduling . The quantum genetic algorithm was proposed in the paper by taking the advantage of quantum computing combined with the traditional genetic algorithm , and the scheduling method based on critical tasks was used to solve scheduling problems of task after division . Experimental results show that the quantum genetic algorithm increases the diversity of population in the process of task partitioning and the task scheduling algorithm based on the key tasks determines the optimal execution order . In this paper we make a contribution for scheduling problems solving through the web , by using web service . The XML-based modeling and communication is applied to the production scheduling activity . The other one deals with Runway Operations Planning and Control . In the Transportation , related sessions of the Minitrack there are two papers dealing with scheduling type problems . One of them proposes a solution of Real World Train Timetabling Problems , the other deals with Dynamic Vehicle Dispatching using several techniques such as agents and genetic programming . The different variants of algorithm are evaluated by a randomly generated testbench . Keywords— Dynamic reconfiguration , FPGA , co-design , Partitioning , Scheduling These findings are evidenced by our detailed simulations , which show with C2-Bound the design space can be narrowed down significantly up to four orders of magnitude . C2-Bound analytic results can be either used in reconfigurable hardware environments or , by software designers , applied to scheduling , partitioning , and allocating resources among diverse applications . To overcome this issue , many researchers used fuzzy naive Bayesian classification for partitioning the continuous values . On the other hand , the choice of the distance function is an important subject that should be taken into consideration in fuzzy partitioning or clustering . In this study , a new fuzzy Bayes classifier is proposed for numerical attributes without the independency assumption . Given the measuring problems under current industrial conditions , a non-iterative T – S fuzzy model is established in this study for on-line modeling . Firstly , a random hidden-layer structure for the antecedent part of fuzzy rules is designed for fuzzy partitioning , which improves the efficiency of on-line training and makes the model adaptive for industrial application . A fast sparse coding identification method is then proposed to determine the consequent parameters of the fuzzy rules , which is capable of enhancing the robustness and the generalization performance under various production situations . The partitioning system takes a database , a workload , a node cluster and partitioning constraints as input and outputs a lookup-table encoding the nal database partitioning decision . The database partitioning problem is modeled as a multi-constraints hyper-graph partitioning problem . By deriving a min-cut of the hyper-graph , our system can minimize the total number of distributed transactions in the workload , balance the sizes and workload accesses of the partitions and satisfy all the partition constraints imposed . Moreover , systems with multiple heterogeneous processing units have been widely adopted to enhance the computing capability or reduce the power consumption . This work explores how to synthesize a heterogeneous multiprocessor platform or select processing units with the partitioning of real-time tasks so that the energy consumption is minimized . Given a set of processing unit types , characterized by the power consumption for maintaining activeness and executing jobs , this paper proposes an efficient and effective algorithm to allocate processing units with energy-efficient task partitioning . What Makes Them Tick?.- Invited Talk 3.- Towards Very Large , Self-Managing Distributed Systems.- Distributed and Multiprocessor Algorithms I.- Linear Time Byzantine Self-Stabilizing Clock Synchronization.- Detecting Locally Stable Predicates Without Modifying Application Messages.- Multiple Agents RendezVous in a Ring in Spite of a Black Hole.- Splitters : Objects for Online Partitioning.- Peer-to-Peer Systems , Middleware I.- Partial Replication : Achieving Scalability in Redundant Arrays of Inexpensive Databases.- A Peer-to-Peer Approach to Enhance Middleware Connectivity.- Multicast in Overlay Networks.- Peer-to-Peer Systems , Middleware II.- Real-Time Framework for Distributed Embedded Systems.- Self-Organization Approach of Communities for P2P Networks.- The key challenge to design an efficient stream join system is how to reasonably partition the streaming data among distributed processing nodes to avoid high density of join computation . However , the skewed distribution of real world streams raises great challenges for streaming data partitioning in distributed stream join systems . Existing hash based partitioning schemes incur significant load imbalance which leads to low system throughput and long processing latency , while shuffling based strategies incur redundant join computation and much more communication . Therefore , we propose in this paper , a comparative study between our partitioning approach based on the association rules algorithm and a genetic based one . The study aims to compare the results of the aforementioned approaches in case of OLAP partitioning . A new approach to variable size block matching is proposed , based on the binary partitioning of blocks . If a particular block does not allow for accurate motion compensation , then it is split into two using the horizontal or vertical line that achieves the maximum reduction in motion compensation error . This paper formulates the wavelength assignment issues an interconnecting optical broadcast-star local area networks ( LANs ) through a wavelength routing bridge . Static and dynamic approaches to partitioning of wavelengths for local and global traffic are compared using analysis and simulations . It is found that static wavelength assignment , the easiest algorithm to implement , is significantly outperformed by dynamic algorithms . um BD NewSQL com suporte a streaming de dados , de maneira autônoma , a fim de melhorar o desempenho de transações OLTP e de consultas de streaming . Palavras Chave : data partitioning , NewSQL , OLTP Systems , Automate partitioning , Big Data 2018 SBC 33rd Brazilian Symposium on Databases ( SBBD ) August 25 - 26 , 2018 Rio de Janeiro , RJ , Brazil [ reference ] . For BU we consider a model that ( 1 ) uses a fully - connected graph with up to detection proposals and jointly performs partitioning and body - part labeling similar to ( BU - full , label ) ; ( 2 ) is same as ( 1 ) , but labeling of detection proposals is done based on detection score ( BU - full ) ; ( 3 ) is same as ( 2 ) , but uses a sparsely - connected graph ( BU - sparse ) . The results are shown in Tab . Performing inference ( TD / BU + SP ) improves the performance to % AP , due to more optimal assignment of part detection candidates to corresponding persons . Graph simplification in TD / BU allows to further reduce the inference time for graph partitioning ( vs. for BU - sparse ) . Single Frame Tracking Single Frame Tracking Comparison to the State of the Art . This speed - up is due to two factors . First , TD / BU relies on a faster solver that tackles the graph - partitioning problem via local search , in contrast to the exact solver used in . Second , in the case of TD / BU model the graph is sparse and a large portion of the computation is performed by the feed - forward CNN introduced in Sec . Our approach defines a model that jointly groups body part proposals within each video frame and across time . Grouping is formulated as a graph partitioning problem that lends itself to efficient inference with recent local search techniques . Our approach improves over state - of - the - art while being substantially faster compared to other related work . One approach first aligns records and sentences and then learns a content selection model [ reference ][ reference ] . Hierarchical hidden semi - Markov generative models have also been used to first determine which facts to discuss and then to generate words from the predicates and arguments of the chosen facts [ reference ] . Sentence planning has been formulated as a supervised set partitioning problem over facts where each partition corresponds to a sentence [ reference ] . End - to - end approaches have combined sentence planning and surface realization by using explicitly aligned sentence / meaning pairs as training data [ reference ][ reference ][ reference ][ reference ] . More recently , content selection and surface realization have been combined [ reference ][ reference ][ reference ] . Articulated pose estimation of multiple people in uncontrolled real world images is challenging but of real world interest . In this work , we proposed a new formulation as a joint subset partitioning and labeling problem ( SPLP ) . Different to previous two - stage strategies that separate the detection and pose estimation steps , the SPLP model jointly infers the number of people , their poses , spatial proximity , and part level occlusions . We presented the Generative Partition Network ( GPN ) to efficiently and effectively address the challenging multi - person pose estimation problem . GPN solves the problem by simultaneously detecting and partitioning joints for multiple persons . It introduces a new approach to generate partitions through inferring over joint candidates in the embedding space parameterized by person centroids . For instance , the betweeness centrality of a vertex computes the fractions of shortest paths that pass through . The Weisfeiler - Lehman algorithm is a procedure for partitioning the vertices of a graph . It is also known as color refinement and naive vertex classification . In this work , we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes . The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images , eliminating unnecessary intermediate steps ( e.g. , candidate aggregation and word partitioning ) , with a single neural network . The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture . In real life optimization problems , it is very important to have high quality solutions ( optimal ) . But when uncertainty becomes part of the optimization problem , solutions should be optimal and robust to the uncertain environmental changes . In many real life optimization problems , the parameters are often imprecise and are difficult to be represented in discrete quantity . One of the approaches to model such situation is considering these imprecise parameters as intuitionistic fuzzy numbers and then approximating these by its expected interval value . Differential Evolution ( DE ) is used as the underlying search technique . DE is designed especially for the real optimization problems . We have studied the reduction in the number of function computations and timing requirement achieved with the proposed technique . Using this technique , a temporary symmetry over the neighborhood region is induced . This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version . The results highlight improvements in accuracy , reliability and convergence rate . We also consider the max-min EE problem which maximizes the EE of the worst energy efficient user . For solving the EE optimization , we derive approximate closed-form lower bounds for the ergodic achievable rate for maximal-ratio and zero-forcing processing at the relay by using minimum mean squared error channel estimation . We numerically show the accuracy of the derived lower bounds and the improved GEE with optimal power allocation , with and without QoS constraints . We propose a new transmission scheme involving rationale of beamspace MIMO communications and beam selection . We also develop a low complexity beam selection algorithm and evaluate adaptive selection on the RF configuration for energy efficiency ( EE ) optimization . Our analysis and simulation results show that the proposed scheme can significantly reduce system complexity and achieve good performance on energy efficiency and detection . The fundamental novelty of EE optimization is that it incorporates the transceivers ' processing power in addition to the actual transmit power in the BF design . The key features of the thesis are that it focuses on sophisticated power consumption models ( PCMs ) , giving useful insights into the EE of current cellular systems in particular , and provides mathematical tools for EE optimization in future wireless networks generally . The BF problem is first studied in a multiuser multiple-input single-output system by using a PCM scaling with transmit power and the number of active radio frequency ( RF ) chains . The maximum planar subgraph , maximum outerplanar subgraph , the thickness and outerthickness of a graph are all NP-complete optimization problems . Apptopinv is a program that contains different heuristic algorithms for these four problems : algorithms based on Hopcroft-Tarjan planarity testing algorithm , the spanning-tree heuristic and various algorithms based on the cactus-tree heuristic . Cell formation , machine layout and cell layout problems in cellular manufacturing system are NP-Complete optimization problems . Good cell formation & layout design in cellular manufacturing system is achieved by finding optimum or near-optimum solutions of these problems , which substantially reduces manufacturing cost and time . A parallel distributed processing architecture called an entropy machine ( EM ) is proposed . This machine , which is based on an artificial neural network composed of massive neurons and interconnections , is used for solving a variety of NP-complete optimization problems . The EM performs the parallel distributed gradient descent method or gradient ascent method to search for minima or maxima.<<ETX > > Interference alignment ( IA ) is a well-known transmission strategy for interference channels , with which the sum rate can linearly scale with the number of users at high signal-to-noise ratio ( SNR ) . However , most existing works on IA considered only the maximization of the sum rate without taking into account the bit error rates ( BER ) performance . In this work , we firstly propose a novel IA algorithm to improve the BER performance for MIMO-OFDM interference channels on a per-subcarrier basis . Non-orthogonal multiple access ( NOMA ) is a promising technique for the fifth generation mobile communication system due to its high spectral efficiency ( SE ) . Previous works focus on user scheduling or/and power allocation to achieve the maximization of sum rate . However , the energy efficiency ( EE ) has not been well studied for NOMA systems . Besides , the metric measuring the quality of multi-point transmission is limited . In this paper , we propose a cross-cell coordination scheme which considers both the maximization of sum-rate and the minimization of packet sum-delay in multi-carrier MF-HSDPA network . The proposed scheme involves the cooperation of the cross-cell scheduling and power optimization . We study power control in a multi-cell CDMA wireless system whereby self-interested users share a common spectrum and interfere with each other . Our objective is to design a power control scheme that achieves a ( near ) optimal power allocation with respect to any predetermined network objective ( such as the maximization of sum-rate , or some fairness criterion ) . To obtain this , we introduce the potential-game approach that relies on approximating the underlying noncooperative game with a " close " potential game , for which prices that induce an optimal power allocation can be derived . Abstract In this paper , we apply a gradient neural network model to efficiently solve the convex second-order cone constrained variational inequality problem . According to a smoothing method , the variational inequality problem is first reduced to a convex second order cone programming ( CSOCP ) . Using a capable neural network , the obtained convex programming problem is solved .
Abstract Artificial bee colony ( ABC ) is a swarm optimization algorithm that shows competitive performance for many optimization problems . However , ABC often suffers from poor exploitation and slow convergence .
Unlike many other metaheuristics , it is easy to implement and does not need a lot of parameters . This makes it applicable to a wide range of optimization problems . To evaluate the efficiency of the proposed algorithm , we examined it on the traveling salesman problem ( TSP ) using some benchmark from TSPLIB online library of instances for the TSP .
The research concerns the study of neural algorithms for developing CAD tools with A.I. features in VLSI design activities . In this paper the focus is on optimization problems such as partitioning , placement and routing . These problems require massive computational power to be solved ( NP-complete problems ) and the standard approach is usually based on euristic techniques .
Based on the analyses of the limitation of a common genetic algorithm and the model of the structure and development of human society , the paper discusses a modified genetic algorithm . The algorithm applies unidirectional inheritance and hierarchical structure to solve the optimizing problems . This method can not only reserve the useful genetic information , but also make the succeeding inheritance more goal-oriented .
This work proposes a multi-objective artificial bee colony ( MOABC ) for optimizing problems with multiple objectives . We have adapted the original Artificial Bee Colony ( ABC ) algorithm to multi objective problems with a grid-based approach for maintaining and adaptively assessing the Pareto front .
Most of the complex problems which appear in the manufacturing domain and the management of flexible manufacturing systems are optimizing problems . In design and maintenance activities , engineers have to make series of technological and managing decisions at different levels , the purpose of these decisions is to minimize necessary actions or to maximize the benefit .
We introduce a novel surrogate-assisted Genetic Algorithm ( GA ) for expensive optimization of problems with discrete categorical variables . Specifically , we leverage the strengths of the Gene-pool Optimal Mixing Evolutionary Algorithm ( GOMEA ) , a state-of-the-art GA , and , for the first time , propose to use a convolutional neural network ( CNN ) as a surrogate model .
In this paper , after a beginning on the concept of ant algorithms , a brief survey of the ant-based methods proposed for optimization of problems with continuous design spaces is presented . As a common approach in continuous domains , discretizing the search space is the model presented to be appended to the original ant colony system ( ACS ) algorithm .
Genetic algorithm ( GA ) is a useful method for optimization of problems which their calculations have different steps . Also , it is suitable for problems in which some parameters should be assumed at first .
We use the energy function where are the edges of a 4 - neighborhood graph and is the regularization parameter . Optimization is performed using belief propagation , with the techniques suggested in . Message passing is performed efficiently using the squared Euclidean distance transform .
Additionally , testing requires a careful initialisation for solving a difficult highly non - convex optimization problem , which is slow . The work of , a popular approach for 2.5D reconstruction from a single image , formulates and solves a carefully initialised ( for frontal images only ) non - convex optimization problem for recovering the lighting , depth , and albedo in an alternating manner where each of the sub - problems is a difficult optimization problem per se . In , a quite popular recent approach for creating a neutral subject - specific 2.5D model from a near frontal image , an iterative procedure is proposed which entails localising facial landmarks , face frontalization , solving a photometric stereo problem , local surface normal estimation , and finally shape integration .
The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice . Finally , we found that reversing the order of the words in all source sentences ( but not target sentences ) improved the LSTM ’s performance markedly , because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier . section : Introduction
Presented in this work is the development of a simulation modelling and solution approach for the scheduling problem of a case study company which can be classified as a project based flow shop . The problem is formulated as a Resource Constrained Project Scheduling Problem ( RCPSP ) and solved with a variable intensity approach .
Therefore , we need to deal with large and complex scheduling problems . In this paper , we introduce a model in which several RCPSPs form a whole scheduling problem represented by a directed graph based on a precedence relation . Moreover , resource prices are getting higher recently and we must utilize resources more and more efficiently .
This paper extends the scheduling methodology for complex stochastic networks that is based on the solution of a “ fluid ” relaxation ( FR ) at each decision point of the original scheduling problem to stochastic networks with blocking and deadlocking effects . For a clearer and more concrete treatment , the presented results are developed in the operational context of a re-entrant line ( RL ) with finite buffering capacity at each workstation ; these RLs are characterized as “ capacitated RLs ( CRLs ) .
The problem is known to be NP-complete and because of this , existing buffer minimisation techniques are heuristics and hence not exact . Modern model-checking tools are quite efficient and they have been successfully applied to scheduling-related problems . We study the feasibility of this approach with examples .
Cross-Layer Interactive Framework for Enhancing Network Lifetime and Communication in WSN . Adoption of Cross-Layer based techniques in Wireless Sensor Network ( WSN ) has been reported to be an efficient solution towards improving energy efficiency , scheduling related problems among the resource-limited sensor nodes . The existing approaches are reviewed to ensure that there exists further scope for the improvement and enhancement in terms of resolving the issues associated with even faster interaction among the layers of the conventional protocol stack for catering up and facilitating the demands of faster response time in real-time applications .
In this paper , we study a set of real-time scheduling problems whose objectives can be expressed as piecewise linear utility functions . This model has very wide applications in scheduling-related problems , such as mixed criticality , response time minimization , and tardiness analysis . Approximation schemes and matrix vectorization techniques are applied to transform scheduling problems into linear constraint optimization with a piecewise linear and concave objective ; thus , a neural network-based optimization method can be adopted to solve such scheduling problems efficiently .
This paper describes the Oz Schedulel ; a workbench for schedulingproblems . Through a graphical interface , the user can freely combine the elements that defne a scheduling strategy .
Excluded from consideration in this chapter are paral-lel scheduling models of other typical job characteristics , on-line and randomized algorithms , all of whichare covered in separate chapters of this book . We will address the following aspects of the schedulingproblems : Polynomial solvability and approximability Enumerative algorithms Polynomial-time approximationsAlthough we also consider scheduling jobs of different release dates , we will mainly concentrate onmodels of equal job release dates .
The article is devoted to the study of metaheuristic method for scheduling problems solution . The article describes genetic algorithm successfully applied to solve the problems .
The special role of scheduling in manage-ment of the problems projects is noted . The review and classification of the approaches of the scheduling problems solution is made .
To realize the mention the company must compose production planning hooked on scheduling of production . The scheduling in problems right now , there are excessively long delivery times and the large of total idle time . The Problems cause the height of production cost in company .
As to the high request of low power consumption for hardware/software partitioning algorithm in multi-core processor structure , based on reducing the system power consumption , the relevant formal model was defined , and the problem was decomposed into two periods : the task division of heterogeneous multi-core platform and low power scheduling . The quantum genetic algorithm was proposed in the paper by taking the advantage of quantum computing combined with the traditional genetic algorithm , and the scheduling method based on critical tasks was used to solve scheduling problems of task after division . Experimental results show that the quantum genetic algorithm increases the diversity of population in the process of task partitioning and the task scheduling algorithm based on the key tasks determines the optimal execution order .
In this paper we make a contribution for scheduling problems solving through the web , by using web service . The XML-based modeling and communication is applied to the production scheduling activity .
The other one deals with Runway Operations Planning and Control . In the Transportation , related sessions of the Minitrack there are two papers dealing with scheduling type problems . One of them proposes a solution of Real World Train Timetabling Problems , the other deals with Dynamic Vehicle Dispatching using several techniques such as agents and genetic programming .
The different variants of algorithm are evaluated by a randomly generated testbench . Keywords— Dynamic reconfiguration , FPGA , co-design , Partitioning , Scheduling
These findings are evidenced by our detailed simulations , which show with C2-Bound the design space can be narrowed down significantly up to four orders of magnitude . C2-Bound analytic results can be either used in reconfigurable hardware environments or , by software designers , applied to scheduling , partitioning , and allocating resources among diverse applications .
To overcome this issue , many researchers used fuzzy naive Bayesian classification for partitioning the continuous values . On the other hand , the choice of the distance function is an important subject that should be taken into consideration in fuzzy partitioning or clustering . In this study , a new fuzzy Bayes classifier is proposed for numerical attributes without the independency assumption .
Given the measuring problems under current industrial conditions , a non-iterative T – S fuzzy model is established in this study for on-line modeling . Firstly , a random hidden-layer structure for the antecedent part of fuzzy rules is designed for fuzzy partitioning , which improves the efficiency of on-line training and makes the model adaptive for industrial application . A fast sparse coding identification method is then proposed to determine the consequent parameters of the fuzzy rules , which is capable of enhancing the robustness and the generalization performance under various production situations .
The partitioning system takes a database , a workload , a node cluster and partitioning constraints as input and outputs a lookup-table encoding the nal database partitioning decision . The database partitioning problem is modeled as a multi-constraints hyper-graph partitioning problem . By deriving a min-cut of the hyper-graph , our system can minimize the total number of distributed transactions in the workload , balance the sizes and workload accesses of the partitions and satisfy all the partition constraints imposed .
Moreover , systems with multiple heterogeneous processing units have been widely adopted to enhance the computing capability or reduce the power consumption . This work explores how to synthesize a heterogeneous multiprocessor platform or select processing units with the partitioning of real-time tasks so that the energy consumption is minimized . Given a set of processing unit types , characterized by the power consumption for maintaining activeness and executing jobs , this paper proposes an efficient and effective algorithm to allocate processing units with energy-efficient task partitioning .
What Makes Them Tick?.- Invited Talk 3.- Towards Very Large , Self-Managing Distributed Systems.- Distributed and Multiprocessor Algorithms I.- Linear Time Byzantine Self-Stabilizing Clock Synchronization.- Detecting Locally Stable Predicates Without Modifying Application Messages.- Multiple Agents RendezVous in a Ring in Spite of a Black Hole.- Splitters : Objects for Online Partitioning.- Peer-to-Peer Systems , Middleware I.- Partial Replication : Achieving Scalability in Redundant Arrays of Inexpensive Databases.- A Peer-to-Peer Approach to Enhance Middleware Connectivity.- Multicast in Overlay Networks.- Peer-to-Peer Systems , Middleware II.- Real-Time Framework for Distributed Embedded Systems.- Self-Organization Approach of Communities for P2P Networks.-
The key challenge to design an efficient stream join system is how to reasonably partition the streaming data among distributed processing nodes to avoid high density of join computation . However , the skewed distribution of real world streams raises great challenges for streaming data partitioning in distributed stream join systems . Existing hash based partitioning schemes incur significant load imbalance which leads to low system throughput and long processing latency , while shuffling based strategies incur redundant join computation and much more communication .
Therefore , we propose in this paper , a comparative study between our partitioning approach based on the association rules algorithm and a genetic based one . The study aims to compare the results of the aforementioned approaches in case of OLAP partitioning .
A new approach to variable size block matching is proposed , based on the binary partitioning of blocks . If a particular block does not allow for accurate motion compensation , then it is split into two using the horizontal or vertical line that achieves the maximum reduction in motion compensation error .
This paper formulates the wavelength assignment issues an interconnecting optical broadcast-star local area networks ( LANs ) through a wavelength routing bridge . Static and dynamic approaches to partitioning of wavelengths for local and global traffic are compared using analysis and simulations . It is found that static wavelength assignment , the easiest algorithm to implement , is significantly outperformed by dynamic algorithms .
um BD NewSQL com suporte a streaming de dados , de maneira autônoma , a fim de melhorar o desempenho de transações OLTP e de consultas de streaming . Palavras Chave : data partitioning , NewSQL , OLTP Systems , Automate partitioning , Big Data 2018 SBC 33rd Brazilian Symposium on Databases ( SBBD ) August 25 - 26 , 2018 Rio de Janeiro , RJ , Brazil
[ reference ] . For BU we consider a model that ( 1 ) uses a fully - connected graph with up to detection proposals and jointly performs partitioning and body - part labeling similar to ( BU - full , label ) ; ( 2 ) is same as ( 1 ) , but labeling of detection proposals is done based on detection score ( BU - full ) ; ( 3 ) is same as ( 2 ) , but uses a sparsely - connected graph ( BU - sparse ) . The results are shown in Tab .
Performing inference ( TD / BU + SP ) improves the performance to % AP , due to more optimal assignment of part detection candidates to corresponding persons . Graph simplification in TD / BU allows to further reduce the inference time for graph partitioning ( vs. for BU - sparse ) . Single Frame Tracking Single Frame Tracking Comparison to the State of the Art .
This speed - up is due to two factors . First , TD / BU relies on a faster solver that tackles the graph - partitioning problem via local search , in contrast to the exact solver used in . Second , in the case of TD / BU model the graph is sparse and a large portion of the computation is performed by the feed - forward CNN introduced in Sec .
Our approach defines a model that jointly groups body part proposals within each video frame and across time . Grouping is formulated as a graph partitioning problem that lends itself to efficient inference with recent local search techniques . Our approach improves over state - of - the - art while being substantially faster compared to other related work .
One approach first aligns records and sentences and then learns a content selection model [ reference ][ reference ] . Hierarchical hidden semi - Markov generative models have also been used to first determine which facts to discuss and then to generate words from the predicates and arguments of the chosen facts [ reference ] . Sentence planning has been formulated as a supervised set partitioning problem over facts where each partition corresponds to a sentence [ reference ] . End - to - end approaches have combined sentence planning and surface realization by using explicitly aligned sentence / meaning pairs as training data [ reference ][ reference ][ reference ][ reference ] . More recently , content selection and surface realization have been combined [ reference ][ reference ][ reference ] .
Articulated pose estimation of multiple people in uncontrolled real world images is challenging but of real world interest . In this work , we proposed a new formulation as a joint subset partitioning and labeling problem ( SPLP ) . Different to previous two - stage strategies that separate the detection and pose estimation steps , the SPLP model jointly infers the number of people , their poses , spatial proximity , and part level occlusions .
We presented the Generative Partition Network ( GPN ) to efficiently and effectively address the challenging multi - person pose estimation problem . GPN solves the problem by simultaneously detecting and partitioning joints for multiple persons . It introduces a new approach to generate partitions through inferring over joint candidates in the embedding space parameterized by person centroids .
For instance , the betweeness centrality of a vertex computes the fractions of shortest paths that pass through . The Weisfeiler - Lehman algorithm is a procedure for partitioning the vertices of a graph . It is also known as color refinement and naive vertex classification .
In this work , we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes . The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images , eliminating unnecessary intermediate steps ( e.g. , candidate aggregation and word partitioning ) , with a single neural network . The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture .
In real life optimization problems , it is very important to have high quality solutions ( optimal ) . But when uncertainty becomes part of the optimization problem , solutions should be optimal and robust to the uncertain environmental changes .
In many real life optimization problems , the parameters are often imprecise and are difficult to be represented in discrete quantity . One of the approaches to model such situation is considering these imprecise parameters as intuitionistic fuzzy numbers and then approximating these by its expected interval value .
Differential Evolution ( DE ) is used as the underlying search technique . DE is designed especially for the real optimization problems . We have studied the reduction in the number of function computations and timing requirement achieved with the proposed technique .
Using this technique , a temporary symmetry over the neighborhood region is induced . This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version . The results highlight improvements in accuracy , reliability and convergence rate .
We also consider the max-min EE problem which maximizes the EE of the worst energy efficient user . For solving the EE optimization , we derive approximate closed-form lower bounds for the ergodic achievable rate for maximal-ratio and zero-forcing processing at the relay by using minimum mean squared error channel estimation . We numerically show the accuracy of the derived lower bounds and the improved GEE with optimal power allocation , with and without QoS constraints .
We propose a new transmission scheme involving rationale of beamspace MIMO communications and beam selection . We also develop a low complexity beam selection algorithm and evaluate adaptive selection on the RF configuration for energy efficiency ( EE ) optimization . Our analysis and simulation results show that the proposed scheme can significantly reduce system complexity and achieve good performance on energy efficiency and detection .
The fundamental novelty of EE optimization is that it incorporates the transceivers ' processing power in addition to the actual transmit power in the BF design . The key features of the thesis are that it focuses on sophisticated power consumption models ( PCMs ) , giving useful insights into the EE of current cellular systems in particular , and provides mathematical tools for EE optimization in future wireless networks generally . The BF problem is first studied in a multiuser multiple-input single-output system by using a PCM scaling with transmit power and the number of active radio frequency ( RF ) chains .
The maximum planar subgraph , maximum outerplanar subgraph , the thickness and outerthickness of a graph are all NP-complete optimization problems . Apptopinv is a program that contains different heuristic algorithms for these four problems : algorithms based on Hopcroft-Tarjan planarity testing algorithm , the spanning-tree heuristic and various algorithms based on the cactus-tree heuristic .
Cell formation , machine layout and cell layout problems in cellular manufacturing system are NP-Complete optimization problems . Good cell formation & layout design in cellular manufacturing system is achieved by finding optimum or near-optimum solutions of these problems , which substantially reduces manufacturing cost and time .
A parallel distributed processing architecture called an entropy machine ( EM ) is proposed . This machine , which is based on an artificial neural network composed of massive neurons and interconnections , is used for solving a variety of NP-complete optimization problems . The EM performs the parallel distributed gradient descent method or gradient ascent method to search for minima or maxima.<<ETX > >
Interference alignment ( IA ) is a well-known transmission strategy for interference channels , with which the sum rate can linearly scale with the number of users at high signal-to-noise ratio ( SNR ) . However , most existing works on IA considered only the maximization of the sum rate without taking into account the bit error rates ( BER ) performance . In this work , we firstly propose a novel IA algorithm to improve the BER performance for MIMO-OFDM interference channels on a per-subcarrier basis .
Non-orthogonal multiple access ( NOMA ) is a promising technique for the fifth generation mobile communication system due to its high spectral efficiency ( SE ) . Previous works focus on user scheduling or/and power allocation to achieve the maximization of sum rate . However , the energy efficiency ( EE ) has not been well studied for NOMA systems .
Besides , the metric measuring the quality of multi-point transmission is limited . In this paper , we propose a cross-cell coordination scheme which considers both the maximization of sum-rate and the minimization of packet sum-delay in multi-carrier MF-HSDPA network . The proposed scheme involves the cooperation of the cross-cell scheduling and power optimization .
We study power control in a multi-cell CDMA wireless system whereby self-interested users share a common spectrum and interfere with each other . Our objective is to design a power control scheme that achieves a ( near ) optimal power allocation with respect to any predetermined network objective ( such as the maximization of sum-rate , or some fairness criterion ) . To obtain this , we introduce the potential-game approach that relies on approximating the underlying noncooperative game with a " close " potential game , for which prices that induce an optimal power allocation can be derived .
Abstract In this paper , we apply a gradient neural network model to efficiently solve the convex second-order cone constrained variational inequality problem . According to a smoothing method , the variational inequality problem is first reduced to a convex second order cone programming ( CSOCP ) . Using a capable neural network , the obtained convex programming problem is solved .
Fireworks explosion based artificial bee colony for numerical optimization A novel metaheuristic optimization algorithm: the monarchy metaheuristic Neural algorithms on VLSI concurrent architectures A modified genetic algorithm-algorithm description A multi-objective Artificial Bee Colony for optimizing multi-objective problems Optimizing material flows into the architecture of a flexible system of integrated manufacturing CIM Convolutional neural network surrogate-assisted GOMEA EVALUATING THE DISCRETIZATION OF SEARCH SPACE IN CONTINUOUS PROBLEMS FOR ANT COLONY OPTIMIZATION Optimal column base plate design using a modified genetic algorithm based on Newton-Raphson method Do Convnets Learn Correspondence? Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Sequence to Sequence Learning with Neural Networks Variable intensity RCPSP approach to a case study flow shop Resource Transfer Planning in a Group of RCPSPs Forming a Directed Graph Throughput Maximization of Capacitated Re-Entrant Lines Through Fluid Relaxation Minimising buffer requirements of synchronous dataflow graphs with model checking CLIF: Cross-Layer Interactive Framework for Enhancing Network Lifetime and Communication in WSN A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility Oz Scheduler: A Workbench for Scheduling Problems Parallel Scheduling for Early Completion Variational Genetic Algorithm for NP-hard Scheduling Problem Solution Программные методы и средства планирования и управления проектами Analisis Perbandingan Pada Penjadwalan Job Untuk Ketepatan Waktu Proses Study on a New Heterogeneous Multi-core Hardware/Software Partitioning Method Scheduling Decision-Making Using Web Service Intelligent Systems in Traffic and Transportation A Hardware-Software Partitioning and Scheduling Algorithm for Dynamically Reconfigurable Embedded Systems C2-bound: a capacity and concurrency driven analytical model for many-core design A Fuzzy Bayesian Classifier with Learned Mahalanobis Distance Non-iterative T–S fuzzy modeling with random hidden-layer structure for BFG pipeline pressure prediction Hyper-Graph Based Database Partitioning for Task Partitioning and Platform Synthesis for Energy Efficiency Principles of distributed systems : 7th International Conference, OPODIS 2003, La Martinique, French West Indies, December 10-13, 2003 : revised selected papers Simois: A Scalable Distributed Stream Join System with Skewed Workloads Comparative Analysis of Our Association Rules Based Approach and a Genetic Approach for OLAP Partitioning Motion-compensation using variable-size block-matching with binary partition trees All-optical LAN interconnection with a wavelength selective router An Autonomous Hybrid Data Partition for NewSQL DBs ArtTrack: Articulated Multi-Person Tracking in the Wild Neural Text Generation from Structured Data with Application to the Biography Domain DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation Generative Partition Networks for Multi-Person Pose Estimation Learning Convolutional Neural Networks for Graphs EAST: An Efficient and Accurate Scene Text Detector Using multiobjective metaheuristics to solve VRP with uncertain demands Linear Programming Problem with Intuitionistic Fuzzy numbers An Approach for the Ordering of Evaluation of Objectives in Multiobjective Optimization Symmetry Induction in Computational Intelligence Energy-Efficient Multi-Pair Two-Way AF Full-Duplex Massive MIMO Relaying Energy Efficient Hybrid Precoding for Multi-User Massive MIMO Systems Using Low-Resolution ADCs Transceiver optimization for energy-efficient multiantenna cellular networks Apptopinv User's Guide Approaches to solve cell formation, machine layout and cell layout problem: A Review Parallel distributed gradient descent and ascent methods Joint Iterative Interference Alignment and SCMA Technique for MIMO-OFDM Systems On energy efficiency optimization in downlink MIMO-NOMA A cross-cell coordination scheme in multi-carrier MF-HSDPA network Near-Optimal Power Control in Wireless Networks: A Potential Game Approach A novel gradient-based neural network for solving convex second-order cone constrained variational inequality problems
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GECCO NIPS 2017 IEEE International Conference on Computer Vision (ICCV) NIPS SummerSim DAC Handbook of Scheduling ICETE (1) SC RTCSA ICDCS NETYS ICIP (1) INFOCOM SBBD Companion 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) EMNLP 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Congress on Evolutionary Computation BIC-TA (1) SiPS VTC Spring ICC Workshops ICC INFOCOM
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