To this end , we conducted experiments with linear congruential generators of various period lengths , a Mersenne Twister with artificially reduced period lengths as well as quasi-random numbers as the source of randomness . Both heuristics were used to solve several instances of the Traveling Salesman Problem in order to compare optimization results . Our experiments show that both Simulated Annealing and the Genetic Algorithm produce inferior solutions when using random numbers with small period lengths or quasi-random numbers of inappropriate dimension . In this work we investigate two variants of the Stochastic Vehicle Routing Problem : The Vehicle Routing Problem with Stochastic Demands and the Vehicle Routing Problem with Stochastic Demands and Customers . We show that under some moderate conditions there is an asymptotic equivalence between the Vehicle Routing Problem with Stochastic Demands and the Traveling Salesman Problem , as well as between the Vehicle Routing Problem with Stochastic Demands and Customers and the Probabilistic Traveling Salesman Problem . Based on our results we give explanations for different observations in literature and we provide ideas for the development of new approximation algorithms and heuristics for these problems . The proposed methodolog y comprises five phases , namely , Problem Characteristics Screening , Clustering , Parameter Screening , Response Surface Modeling and Optimization . The proposed methodology is applied to the Ant Colony System algorithm for solving 47 traveling salesman problem instances . For validation of the proposed methodology , the different alternative approaches for parameter tuning are compared and it is concluded that , the methodology presents better results than the other alternative approaches . Also , the effect of using local search was analyzed . We have studied the performance of this new algorithm for several Traveling Salesman Problem instances . Experimental results obtained conclude that the Two-Stage approach significantly improves the Ant System and Ant Colony System in terms of the computation time needed . This paper develops cloud based software computing as a service ( SCaaS ) in the hybrid evolution algorithm with feedback assistance to solve the data optimal NP-complete problems such as travel salesman problems and job shop scheduling problems . Suitable steps , methods , or procedures of the genetic algorithm can be adopted from various evolution procedures or methods of the genetic algorithms based on the fitness evaluation results and survival ratio of different crossover methods in the current generation . This paper develops a cloud based parallel and distributed evolutionary hybrid algorithm with feedback assistance to help planners solve the data optimal problems such as travel salesman problems . Each step and type of evolution algorithm is established via various virtual machines in cloud . The objective is to minimize the sum of the costs for sending traffic along the links connecting the various nodes . Like many combinatorial problems , thep-hub location problem has many local optima . Heuristics , such as exchange methods , can terminate once such a local optimum is encountered . Phase transitions typically occur in combinatorial computational problems and have important consequences , especially with the current spread of statistical relational learning and of sequence learning methodologies . In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon and the extensive experimental investigation that supports its presence . The problems to optimize properties of a frustrated system of many variables is a complex matter . Such problems arise in both combinatorial computational problems as well as in complex materials such as spin glasses . Since the theoretical treatment of such system is very hard and complicated one would like to find simpler methods to examine them . Many combinatorial computational problems have been effectively solved by means of hyper-heuristics . In this study , we focus on a problem proposed by Kiwi.com and solve this problem by implementing a Reinforcement Learning ( RL ) hyperheuristic algorithm . This paper is a review of the approachesdeveloped to solve 2D packing problems withmeta-heuristic algorithms . As packing tasks arecombinatorial problems with very large searchspaces , the recent literature encourages theuse of meta-heuristic search methods , inparticular genetic algorithms . The objective ofthis paper is to present and categorise thesolution approaches in the literature for 2Dregular and irregular strip packing problems . The exploration of this gradient is done by starting from the optimal tour and later modifying it with a Random Walk process . The aim is to propose a methodology to evaluate the difficulty of instances of Travelling Salesman Problem , which can be applied to other combinatorial-problems instances . As a consequence of this work , a methodology to evaluate the difficulty of Travelling Salesman Problem instances is proposed and confronted to a wide set of instances , and finally a rank of their difficulty is stated . We focus our attention on the transshipment between vessels and land vehicles , in which case the terminal is described as a maritime container terminal . In these container terminals , many combinatorial related problems appear and the solution of one of the problems may affect to the solution of other related problems . For instance , the berth allocation problem can affect to the crane assignment problem and both could also affect to the Container Stacking Problem . In the ABC algorithm proposed for multi-objective optimization , a non-dominated sorting procedure is employed to rank the individuals based on Pareto-dominance rules . Combinatorial type of problems can also be efficiently solved by the ABC algorithm incorporated with a local search compatible with combinatorial type problems . In the second part of the chapter , an application of the ABC algorithm to colormap quantization is presented . Recently , there are a few methods that assume exemplars are the samples that can best represent the whole dataset . However , for , the optimization is a combinatorial problem ( NP - hard ) , which is computationally intractable to solve . Besides , the representation loss is measured by the least square measure , which is sensitive to outliers in data . The system obtained is an executable specification language for all NP problems which shows interesting computational properties . The performance of the system has been tested on a few classical problems , namely graph coloring , Hamiltonian cycle , job-shop scheduling , and on a real-world scheduling application , namely the tournament scheduling problem . In this paper , we address a generalized sequencing problem that determines both specific bin locations for the retrieval requests and sequencing with the chosen bin locations . To solve this new complex combinatorial problem , we introduce heuristic methods and present their computational results . We also derive the cycle time distributions and their mean values for the single command under both squared and non-squared rack shapes For BS clustering problem , we propose an optimal algorithm under given BS sleep mode state that has polynomial complexity . We integrate the optimal clustering solution into the sleeping problem , which is a complex combinatorial problem , and develop a joint optimal clustering and sleeping algorithm with reduced complexity compared to the exhaustive search . We also develop a greedy algorithm that finds a near-optimal clustering and sleeping solution with polynomial complexity . The Lobby is faced with a profile that specifies for each voter and each issue whether the voter approves or rejects the issue , and seeks to find the smallest set of voters it must influence to change their vote , for a desired outcome to be obtained . This computational problem also describes problems arising in other scenarios of aggregating complex opinions , such as principal-agents incentives scheme in a complex combinatorial problem , and bribery and manipulation in Truth-Functional judgement aggregation . We study the computational complexity of Optimal Lobbying when the issues are aggregated using an anonymous monotone function and the family of desired outcomes is an upward-closed family . The objective function is defined by using the solution to the H2 optimization problem . It is shown that a greedy algorithm gives the global optimal solution to the finite combinatorial problem within a practical time . The algorithm can be understood that we eliminate input variables in ascending order of the average controllability centralities which assign relative importances to each node within a network . Besides this , the model combines multi-agent technology to further improve parallelism , enabling distributed resources to be used more efficiently . This paper uses the classic algorithm of heterogeneous scheduling in distributed environments , the heterogeneous earliest finish time ( HEFT ) algorithm as an example to apply the model by creating different topologies of the task scheduling graph . And then implement the model with Nomadic Pict using channels to transmit information and assigning tasks to multiple agents . Recent advances in machine learning techniques including artificial neural networks have led to the development of powerful and practical prediction models for a variety of fields . As of yet , however , no significant leaps have been taken towards employing machine learning for heterogeneous scheduling in order to maximize system throughput . The core issue we approach is how to understand and utilize the rise of heterogeneous architectures , benefits of heterogeneous scheduling , and the promise of machine learning techniques with respect to maximizing system performance . We propose and study in detail the scheduling and replacement policies of the hierarchical multimedia storage server . We illustrate the le layout policy in the parallel disks system , the scheduling of the tape subsystem , the video object replacement algorithms and the read/write scheduling of the disk system . In this paper , we study in detail the design issues of a hierarchical multimedia storage server that consists of a disk array for caching and a robotic tape library for the storage of all the video objects . We propose and study in detail the file layout policy in the disk subsystem , the scheduling of the tape subsystem , the video object replacement algorithms , the read/write scheduling of the disk subsystem and the memory buffer requirement . CTTPO deals with trade-off between time and cost for the task partitioning and offloading . The MTS algorithm deals with time efficient scheduling on multi-sites , and ESM algorithm saves the energy on the multi-sites by switching the sites from high voltage to low voltage during ideal time . The simulation study demonstrates that the proposed algorithms outperformed the existing techniques based on time , cost and energy parameters . Although many algorithms have been proposed to address the problem , most of them are inefficient for collision and deadlock avoidance in dynamic environments . This paper proposes a dynamic resource reservation ( DRR ) based method supporting time-efficient scheduling and collision avoidance of multiple AGVs . In this method , the layout is divided into square blocks with the same size that are abstracted as points in the undirected graph . Activity scheduling deals with the way of rotating the role of each node among a set of given operation modes ( e.g. dominating nodes versus dominated nodes ) . In this paper , we propose to apply the notion of power-aware connected dominating set to broadcasting and activity scheduling . The effectiveness of the proposed method in prolonging the life span of the network is confirmed through simulation . As these systems evolve , they will be used to run sophisticated applications many of which will involve data whose consistency must be maintained and data updates may originate at mobile client . In pull-based broadcast environment we propose integrated approach to broadcast data scheduling and transaction processing . Our approach aims at reducing tuning time , commit time and number of aborts for the transaction . In market-based service-oriented grids , scheduling service execution should account both for user- and provider-dependent Qualityof-Service ( QoS ) requirements . In this scenario we propose a mechanism to allow for flexible provision of grid services , i.e. to allow providers to dynamically adapt the execution of services according to both the changing conditions of the environment where they operate in , and the requirements of service users . However , these services are paid and need a lower cost-efficient process for the users . In this paper , this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system . The objective was to offer omnipresent cloud services to the generated data with minimum cost . The IOCA-PSO method includes the inner cellular model ( ICM ) , the outer cellular model ( OCM ) , and the fitness function . Our work can be divided into following parts : ( 1 ) Concise basic transition rules and affiliated transition rules are proposed in ICM , which can help the proposed phase cycle planning ( PCP ) algorithm achieve a globally sophisticated scheduling and offer effective solutions for different traffic problems ; ( 2 ) Benefited from the combination of cellular automaton ( CA ) and particle swarm optimization ( PSO ) , the proposed inner and outer cellular PSO ( IOPSO ) algorithm in OCM offers a strong search ability to find out the optimal timing control ; ( 3 ) The proposed fitness function can evaluate and conduct the optimization of traffic lights ’ scheduling dynamically for different aims by adjusting parameters . Extensive experiments show that , compared with the PSO method , the genetic algorithm method and the RANDOM method in real cases , IOCA-PSO presents distinct improvements under different traffic conditions , which shows a high adaptability of the proposed method in urban traffic network scales under different traffic flow states , intersection numbers , and vehicle numbers . In contrast to prior work in this area , it generalizes federated scheduling and proposes a novel reservation-based approach . Namely , we propose a reservation-based federated scheduling strategy that reduces the problem of scheduling arbitrary-deadline DAG task sets to the problem of scheduling arbitrary-deadline sequential task sets by allocating reservation servers . We provide the general reservation design for sporadic parallel tasks , such that any scheduling algorithm and analysis for sequential tasks with arbitrary deadlines can be used to execute the allocated reservation servers of parallel tasks . The main objective of this project was to examine current scheduling practices at transit systems in Florida and assess each agency 's scheduling issues and potential need for technical assistance in schedule development . A scheduling survey was designed and distributed to all Florida fixed route transit systems . The results of this survey are presented and analyzed with a specific emphasis on comparing systems that use automated scheduling software versus those that continue to schedule using manual processes . We consider the communication complexity of finding an approximate maximum matching in a graph in a multi-party message-passing communication model . The maximum matching problem is one of the most fundamental graph combinatorial problems , with a variety of applications . The input to the problem is a graph G that has n vertices and the set of edges partitioned over k sites , and an approximation ratio parameter $ $ \alpha $ $ α . This bound is shown to be tight up to a $ $ \log n$$ log n factor , by constructing an algorithm , establishing its correctness , and an upper bound on the communication cost . The lower bound also applies to other graph combinatorial problems in the message-passing communication model , including max-flow and graph sparsification . Consequently solving a periodically specified problem by explicitly expanding the instance is prohibitively expensive in terms of computational resources . This leads one to investigate the complexity and efficient approximability of solving graph theoretic and combinatorial problems when instances are specified using two dimensional periodic specifications . They prove the following results : ( 1 ) several classical NP-hard optimization problems become NEXPTIME-hard , when instances are specified using two dimensional periodic specifications ; ( 2 ) in contrast , several of these NEXPTIME-hard problems have polynomial time approximation algorithms with guaranteed worst case performance . We can analytically study interesting aspects of real-world complex systems such as the Internet , social systems , transportation networks , and biological interaction data by modeling them as graphs . Graph-theoretic and combinatorial problems are also pervasive in scientific computing and engineering applications . In this dissertation , we address the problem of analyzing large-scale complex networks that represent interactions between hundreds of thousands to billions of entities . An Exponential Complexity Gap between Exact and Approximate Solutions . We study both the complexity and approximability of various graph and combinatorial problems specified using two dimensional narrow periodic specifications ( see CM93,HW92,KMW67,KO91,Or84b , Wa93 ) . The following two general kinds of results are presented . Graph combinatorial optimization problems are of great theoretical and practical importance in Computer Science , Operations Research , and Mathematical Programming . They also arise in many areas of science , technology , and industry . There has always been a need to solve real-life large-scale problems , suchas efficiently allocating limited resources , and other complex and conflicting situations related to combinatorial optimization genre . A class of combinato- rial optimization problems is NP-hard and , among many well-known , several of them are assignment , planning and rescheduling problems . Assignment problems can deal with optimal assignment of teams of collaborating agents ; planning problems can be effects-based planning that search for promising plans to get desired end states with minimal cost ; rescheduling problems can be multi-criteria optimization of rescheduling resources that modify existing original schedule . Vehicle Routing Problem being a combinatorial class of problems has implications in various areas and applications where traditional methods to find a search space either fail or slow down especially in case of real time systems . Using the most popular heuristic searching approach , Genetic Algorithm , there have been solutions in the state of the art to the problems like Vehicle Routing , Traveling Sales Person , etc . Today , finding a viable solution for any real world problem focusing on combinatorial of problems is a crucial task . However , using optimisation techniques , a viable best solution for a specific problem can be obtained , developed and solved despite the existing limitations of the implemented technique . Abstract This paper presents the main results obtained in the field of approximation algorithms in a unified framework . Most of these results have been revisited in order to emphasize two basic tools useful for characterizing approximation classes , that is , combinatorial properties of problems and approximation preserving reducibilities . In particular , after reviewing the most important combinatorial characterizations of the classes PTAS and FPTAS , we concentrate on the class APX and , as a concluding result , we show that this class coincides with the class of optimization problems which are reducible to the maximum satisfiability problem with respect to a polynomial-time approximation preserving reducibility . The tasks include : the automated creation of database , the rapid query of catalogs , cross-match query and the visualization of the queried results . Especially for cross-matching service , many choices are provided for users , such as one-to-one entry , one-to-many entry , one-to-none entry , none-to-one entry . Meanwhile , the probability of cross-matching is given . This way we expect substantial benefits in computing capabilities in data analysis . As a first development using our solution , we propose a cross-matching service for the Taiwan Extragalactic Astronomical Data Center . In today 's global village , it is critical that the key information tools , such as web search engines , e-Commerce portals and e-Governance , work across multiple natural languages , seamlessly We propose a new flexible architecture – Multilingual Information processing on Relational Architecture ( MIRA ) – that supports the multilingual processing functionality of the primary storage mechanism for such deployments – the relational database systems , effectively and efficiently We propose new linguistic matching operators that enhances the standard lexicographic matching of database systems into phonetic and semantic domains We further show that the performance of the systems may be made language-neutral Our proposed architecture is based on standards and hence amenable for easy implementation in any type of query processing and information retrieval systems In this paper , we present our approach to implement the above architecture and outline the host of research issues that are opened up due to the inherently fuzzy nature of the alternative matching semantics . The modeling of text queries as sequences of embeddings for conducting similarity matching based search within speech features has been recently shown to improve keyword search ( KWS ) performance , especially for the out-of-vocabulary ( OOV ) terms . This technique uses a dynamic time warping based search methodology , converting the KWS problem into a pattern search problem by artificially modeling the text queries as pronunciation-based embedding sequences . Firstly , the implementing framework of SDCM is constructed . Then , combined with the theories of ontology and dynamic description logic , a semantics-based SDCM algorithm is designed , which includes four implementation stages , respectively , basic information matching , IOPE parameters ( Input , Outputs , Preconditions , Effects ) matching , QoS ( Quality of Service ) matching and comprehensive matching . Finally , a case verifies the feasibility and effectiveness of the proposed method . The decision where to run which part of the application has to take into account the current latencies to the remote resources and the dynamics of their availability and usage . This requires a Resource Management System that is able to discover matching remote resources fast and efficiently without delaying the offloading procedure too much . Subsequently , we have to determine when or under which conditions which parts shall be offloaded . The invention discloses a sensor code matching processing method and device , a network platform device , and a gateway of the internet of things . The method includes the following steps of ; receiving a code matching application message for requesting code matching of a sensor , wherein the code matching application message carries identification information for identifying the sensor ; determining a code matching state of the sensor according to the identification information ; and sending the determined code matching state to the gateway of the internet of things , wherein the code matching state is used for that the gateway of the internet of things performs code matching processing on the sensor . According to the method , the problem in correlation techniques that potential risk exists in the process of code matching of the sensor due to illegal intruders can be solved , so that the security of sensor access can be effectively improved . Windowed subsequence matching over deterministic strings has been studied in previous work in the contexts of knowledge discovery , data mining , and molecular biology . However , we observe that in these applications , as well as in data stream monitoring , complex event processing , and time series data processing in which streams can be mapped to strings , the strings are often noisy and probabilistic . Our system uses STIX to express threat information , including CoAs . We identified the problem of matching CoAs with the actions that a response system can carry out as a major design challenge , and found a robust and scalable decentralized solution to that challenge by adopting a publish-subscribe model . We built a solution based on the Extensible Messaging and Presence Protocol ( XMPP ) architecture and communications protocol as it provided the right security properties as well as the needed extensibility for both data model and transport protocols . The enhanced eText is also available to buy separately and works on desktops and tablets . Follows the organizational structure of the new Biology guide , with a focus on the Essential Ideas , Understanding , Applications & Skills for complete syllabus-matching . Written by the highly experienced IB author team of Alan Damon , Randy McGonegal , Patricia Tosto and William Ward , you can be confident that you and your students have all the resources you will need for the new Biology curriculum . Given an undirected/directed large weighted data graph and a similar smaller weighted pattern graph , the problem of weighted subgraph matching is to find a mapping of the nodes in the pattern graph to a subset of nodes in the data graph such that the sum of edge weight differences is minimum . Biological interaction networks such as protein-protein interaction networks and molecular pathways are often modeled as weighted graphs in order to account for the high false positive rate occurring intrinsically during the detection process of the interactions . We have explored other design choice of the distance metric . We conduct experiments using discriminator feature distance in our regularization term in a way similar to feature matching loss , where denotes the layer of the discriminator . We apply it to Pix2Pix on the facades dataset . We note that a similar loss is used in VAE - GANs . Our full objective combines both GAN loss and feature matching loss as : where controls the importance of the two terms . Note that for the feature matching loss , only serves as a feature extractor and does not maximize the loss . Image - space loss . Our next baseline controls for the feature matching loss used to train the CRN . Here we use exactly the same architecture as in Section 3 , but use only the first layer Φ 0 ( image color ) in the loss : As a tradeoff , Fischer and Ilg perform feature matching only at a reduced spatial resolution . We reduce the computational burden of feature matching by using a short - ranged matching of warped CNN features at sampled positions and a sub - pixel refinement at every pyramid level . We are inspired by the feature transformation used in Spatial Transformer . instance - level model alignments for 6D object pose estimation as well as to find surface correspondences in 3D meshes . To facilitate further research in the area of 3D keypoint matching and geometric registration , we provide a correspondence matching benchmark as well as a surface registration benchmark similar to [ reference ] , but with real - world scan data . section : Related Work We find keypoints whose descriptors are mutually closest to each other in Euclidean space , and use RANSAC over the 3D positions of these keypoint matches to estimate a rigid transformation between the two point clouds . section : Matching Local Geometry in Scenes We evaluate our 3DMatch - based geometric registration algorithm ( i.e. , 3DMatch + RANSAC ) on both real and synthetic datasets . Our best score on this task is 87.3 % accuracy obtained with the full tree matching NTI model . The previous best performing model on the task performs phrase matching by using the attention mechanism . Our results show that NTI - SLSTM improved the performance of the sequential LSTM encoder by approximately 2 % . We use new techniques to find new bounds , narrow some of the gaps between the lower bounds and upper bounds and find new deterministic and randomized algorithms with small query and time complexities . Some of the bounds are tight in the sense that finding better ones either gives a breakthrough result in some long-standing combinatorial open problem or needs a new technique that is beyond the existing ones . We propose the online-decomposition algorithm ( ODA ) based on offline preprocessing that optimizes each static problem component for discretized values of shared constraint variables , and approximate the optimal aggregated utility functions . ODA uses the pre-processed approximated aggregated cost functions to decompose the original problem into smaller problems , and utilizes search heuristics for the combinatorial part of the problem based on the pre-processed look-up tables . We also conduct an initial experimental evaluation which shows that ODA , as compared with MILP , provides an order of magnitude improvement in terms of both computational time and the quality of found solutions for a class of problems for which preprocessing is possible . The composition model allows set union and difference among the selected elements , while regularizing the problem by restricting their count to a fixed level . This is a combinatorial problem addressing which is not in general computationally tractable . Convex cardinal shape composition ( CSC ) is a recent relaxation scheme presented as a proxy to the original problem .
To this end , we conducted experiments with linear congruential generators of various period lengths , a Mersenne Twister with artificially reduced period lengths as well as quasi-random numbers as the source of randomness . Both heuristics were used to solve several instances of the Traveling Salesman Problem in order to compare optimization results . Our experiments show that both Simulated Annealing and the Genetic Algorithm produce inferior solutions when using random numbers with small period lengths or quasi-random numbers of inappropriate dimension .
In this work we investigate two variants of the Stochastic Vehicle Routing Problem : The Vehicle Routing Problem with Stochastic Demands and the Vehicle Routing Problem with Stochastic Demands and Customers . We show that under some moderate conditions there is an asymptotic equivalence between the Vehicle Routing Problem with Stochastic Demands and the Traveling Salesman Problem , as well as between the Vehicle Routing Problem with Stochastic Demands and Customers and the Probabilistic Traveling Salesman Problem . Based on our results we give explanations for different observations in literature and we provide ideas for the development of new approximation algorithms and heuristics for these problems .
The proposed methodolog y comprises five phases , namely , Problem Characteristics Screening , Clustering , Parameter Screening , Response Surface Modeling and Optimization . The proposed methodology is applied to the Ant Colony System algorithm for solving 47 traveling salesman problem instances . For validation of the proposed methodology , the different alternative approaches for parameter tuning are compared and it is concluded that , the methodology presents better results than the other alternative approaches .
Also , the effect of using local search was analyzed . We have studied the performance of this new algorithm for several Traveling Salesman Problem instances . Experimental results obtained conclude that the Two-Stage approach significantly improves the Ant System and Ant Colony System in terms of the computation time needed .
This paper develops cloud based software computing as a service ( SCaaS ) in the hybrid evolution algorithm with feedback assistance to solve the data optimal NP-complete problems such as travel salesman problems and job shop scheduling problems . Suitable steps , methods , or procedures of the genetic algorithm can be adopted from various evolution procedures or methods of the genetic algorithms based on the fitness evaluation results and survival ratio of different crossover methods in the current generation .
This paper develops a cloud based parallel and distributed evolutionary hybrid algorithm with feedback assistance to help planners solve the data optimal problems such as travel salesman problems . Each step and type of evolution algorithm is established via various virtual machines in cloud .
The objective is to minimize the sum of the costs for sending traffic along the links connecting the various nodes . Like many combinatorial problems , thep-hub location problem has many local optima . Heuristics , such as exchange methods , can terminate once such a local optimum is encountered .
Phase transitions typically occur in combinatorial computational problems and have important consequences , especially with the current spread of statistical relational learning and of sequence learning methodologies . In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon and the extensive experimental investigation that supports its presence .
The problems to optimize properties of a frustrated system of many variables is a complex matter . Such problems arise in both combinatorial computational problems as well as in complex materials such as spin glasses . Since the theoretical treatment of such system is very hard and complicated one would like to find simpler methods to examine them .
Many combinatorial computational problems have been effectively solved by means of hyper-heuristics . In this study , we focus on a problem proposed by Kiwi.com and solve this problem by implementing a Reinforcement Learning ( RL ) hyperheuristic algorithm .
This paper is a review of the approachesdeveloped to solve 2D packing problems withmeta-heuristic algorithms . As packing tasks arecombinatorial problems with very large searchspaces , the recent literature encourages theuse of meta-heuristic search methods , inparticular genetic algorithms . The objective ofthis paper is to present and categorise thesolution approaches in the literature for 2Dregular and irregular strip packing problems .
The exploration of this gradient is done by starting from the optimal tour and later modifying it with a Random Walk process . The aim is to propose a methodology to evaluate the difficulty of instances of Travelling Salesman Problem , which can be applied to other combinatorial-problems instances . As a consequence of this work , a methodology to evaluate the difficulty of Travelling Salesman Problem instances is proposed and confronted to a wide set of instances , and finally a rank of their difficulty is stated .
We focus our attention on the transshipment between vessels and land vehicles , in which case the terminal is described as a maritime container terminal . In these container terminals , many combinatorial related problems appear and the solution of one of the problems may affect to the solution of other related problems . For instance , the berth allocation problem can affect to the crane assignment problem and both could also affect to the Container Stacking Problem .
In the ABC algorithm proposed for multi-objective optimization , a non-dominated sorting procedure is employed to rank the individuals based on Pareto-dominance rules . Combinatorial type of problems can also be efficiently solved by the ABC algorithm incorporated with a local search compatible with combinatorial type problems . In the second part of the chapter , an application of the ABC algorithm to colormap quantization is presented .
Recently , there are a few methods that assume exemplars are the samples that can best represent the whole dataset . However , for , the optimization is a combinatorial problem ( NP - hard ) , which is computationally intractable to solve . Besides , the representation loss is measured by the least square measure , which is sensitive to outliers in data .
The system obtained is an executable specification language for all NP problems which shows interesting computational properties . The performance of the system has been tested on a few classical problems , namely graph coloring , Hamiltonian cycle , job-shop scheduling , and on a real-world scheduling application , namely the tournament scheduling problem .
In this paper , we address a generalized sequencing problem that determines both specific bin locations for the retrieval requests and sequencing with the chosen bin locations . To solve this new complex combinatorial problem , we introduce heuristic methods and present their computational results . We also derive the cycle time distributions and their mean values for the single command under both squared and non-squared rack shapes
For BS clustering problem , we propose an optimal algorithm under given BS sleep mode state that has polynomial complexity . We integrate the optimal clustering solution into the sleeping problem , which is a complex combinatorial problem , and develop a joint optimal clustering and sleeping algorithm with reduced complexity compared to the exhaustive search . We also develop a greedy algorithm that finds a near-optimal clustering and sleeping solution with polynomial complexity .
The Lobby is faced with a profile that specifies for each voter and each issue whether the voter approves or rejects the issue , and seeks to find the smallest set of voters it must influence to change their vote , for a desired outcome to be obtained . This computational problem also describes problems arising in other scenarios of aggregating complex opinions , such as principal-agents incentives scheme in a complex combinatorial problem , and bribery and manipulation in Truth-Functional judgement aggregation . We study the computational complexity of Optimal Lobbying when the issues are aggregated using an anonymous monotone function and the family of desired outcomes is an upward-closed family .
The objective function is defined by using the solution to the H2 optimization problem . It is shown that a greedy algorithm gives the global optimal solution to the finite combinatorial problem within a practical time . The algorithm can be understood that we eliminate input variables in ascending order of the average controllability centralities which assign relative importances to each node within a network .
Besides this , the model combines multi-agent technology to further improve parallelism , enabling distributed resources to be used more efficiently . This paper uses the classic algorithm of heterogeneous scheduling in distributed environments , the heterogeneous earliest finish time ( HEFT ) algorithm as an example to apply the model by creating different topologies of the task scheduling graph . And then implement the model with Nomadic Pict using channels to transmit information and assigning tasks to multiple agents .
Recent advances in machine learning techniques including artificial neural networks have led to the development of powerful and practical prediction models for a variety of fields . As of yet , however , no significant leaps have been taken towards employing machine learning for heterogeneous scheduling in order to maximize system throughput . The core issue we approach is how to understand and utilize the rise of heterogeneous architectures , benefits of heterogeneous scheduling , and the promise of machine learning techniques with respect to maximizing system performance .
We propose and study in detail the scheduling and replacement policies of the hierarchical multimedia storage server . We illustrate the le layout policy in the parallel disks system , the scheduling of the tape subsystem , the video object replacement algorithms and the read/write scheduling of the disk system .
In this paper , we study in detail the design issues of a hierarchical multimedia storage server that consists of a disk array for caching and a robotic tape library for the storage of all the video objects . We propose and study in detail the file layout policy in the disk subsystem , the scheduling of the tape subsystem , the video object replacement algorithms , the read/write scheduling of the disk subsystem and the memory buffer requirement .
CTTPO deals with trade-off between time and cost for the task partitioning and offloading . The MTS algorithm deals with time efficient scheduling on multi-sites , and ESM algorithm saves the energy on the multi-sites by switching the sites from high voltage to low voltage during ideal time . The simulation study demonstrates that the proposed algorithms outperformed the existing techniques based on time , cost and energy parameters .
Although many algorithms have been proposed to address the problem , most of them are inefficient for collision and deadlock avoidance in dynamic environments . This paper proposes a dynamic resource reservation ( DRR ) based method supporting time-efficient scheduling and collision avoidance of multiple AGVs . In this method , the layout is divided into square blocks with the same size that are abstracted as points in the undirected graph .
Activity scheduling deals with the way of rotating the role of each node among a set of given operation modes ( e.g. dominating nodes versus dominated nodes ) . In this paper , we propose to apply the notion of power-aware connected dominating set to broadcasting and activity scheduling . The effectiveness of the proposed method in prolonging the life span of the network is confirmed through simulation .
As these systems evolve , they will be used to run sophisticated applications many of which will involve data whose consistency must be maintained and data updates may originate at mobile client . In pull-based broadcast environment we propose integrated approach to broadcast data scheduling and transaction processing . Our approach aims at reducing tuning time , commit time and number of aborts for the transaction .
In market-based service-oriented grids , scheduling service execution should account both for user- and provider-dependent Qualityof-Service ( QoS ) requirements . In this scenario we propose a mechanism to allow for flexible provision of grid services , i.e. to allow providers to dynamically adapt the execution of services according to both the changing conditions of the environment where they operate in , and the requirements of service users .
However , these services are paid and need a lower cost-efficient process for the users . In this paper , this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system . The objective was to offer omnipresent cloud services to the generated data with minimum cost .
The IOCA-PSO method includes the inner cellular model ( ICM ) , the outer cellular model ( OCM ) , and the fitness function . Our work can be divided into following parts : ( 1 ) Concise basic transition rules and affiliated transition rules are proposed in ICM , which can help the proposed phase cycle planning ( PCP ) algorithm achieve a globally sophisticated scheduling and offer effective solutions for different traffic problems ; ( 2 ) Benefited from the combination of cellular automaton ( CA ) and particle swarm optimization ( PSO ) , the proposed inner and outer cellular PSO ( IOPSO ) algorithm in OCM offers a strong search ability to find out the optimal timing control ; ( 3 ) The proposed fitness function can evaluate and conduct the optimization of traffic lights ’ scheduling dynamically for different aims by adjusting parameters . Extensive experiments show that , compared with the PSO method , the genetic algorithm method and the RANDOM method in real cases , IOCA-PSO presents distinct improvements under different traffic conditions , which shows a high adaptability of the proposed method in urban traffic network scales under different traffic flow states , intersection numbers , and vehicle numbers .
In contrast to prior work in this area , it generalizes federated scheduling and proposes a novel reservation-based approach . Namely , we propose a reservation-based federated scheduling strategy that reduces the problem of scheduling arbitrary-deadline DAG task sets to the problem of scheduling arbitrary-deadline sequential task sets by allocating reservation servers . We provide the general reservation design for sporadic parallel tasks , such that any scheduling algorithm and analysis for sequential tasks with arbitrary deadlines can be used to execute the allocated reservation servers of parallel tasks .
The main objective of this project was to examine current scheduling practices at transit systems in Florida and assess each agency 's scheduling issues and potential need for technical assistance in schedule development . A scheduling survey was designed and distributed to all Florida fixed route transit systems . The results of this survey are presented and analyzed with a specific emphasis on comparing systems that use automated scheduling software versus those that continue to schedule using manual processes .
We consider the communication complexity of finding an approximate maximum matching in a graph in a multi-party message-passing communication model . The maximum matching problem is one of the most fundamental graph combinatorial problems , with a variety of applications . The input to the problem is a graph G that has n vertices and the set of edges partitioned over k sites , and an approximation ratio parameter $ $ \alpha $ $ α .
This bound is shown to be tight up to a $ $ \log n$$ log n factor , by constructing an algorithm , establishing its correctness , and an upper bound on the communication cost . The lower bound also applies to other graph combinatorial problems in the message-passing communication model , including max-flow and graph sparsification .
Consequently solving a periodically specified problem by explicitly expanding the instance is prohibitively expensive in terms of computational resources . This leads one to investigate the complexity and efficient approximability of solving graph theoretic and combinatorial problems when instances are specified using two dimensional periodic specifications . They prove the following results : ( 1 ) several classical NP-hard optimization problems become NEXPTIME-hard , when instances are specified using two dimensional periodic specifications ; ( 2 ) in contrast , several of these NEXPTIME-hard problems have polynomial time approximation algorithms with guaranteed worst case performance .
We can analytically study interesting aspects of real-world complex systems such as the Internet , social systems , transportation networks , and biological interaction data by modeling them as graphs . Graph-theoretic and combinatorial problems are also pervasive in scientific computing and engineering applications . In this dissertation , we address the problem of analyzing large-scale complex networks that represent interactions between hundreds of thousands to billions of entities .
An Exponential Complexity Gap between Exact and Approximate Solutions . We study both the complexity and approximability of various graph and combinatorial problems specified using two dimensional narrow periodic specifications ( see CM93,HW92,KMW67,KO91,Or84b , Wa93 ) . The following two general kinds of results are presented .
Graph combinatorial optimization problems are of great theoretical and practical importance in Computer Science , Operations Research , and Mathematical Programming . They also arise in many areas of science , technology , and industry .
There has always been a need to solve real-life large-scale problems , suchas efficiently allocating limited resources , and other complex and conflicting situations related to combinatorial optimization genre . A class of combinato- rial optimization problems is NP-hard and , among many well-known , several of them are assignment , planning and rescheduling problems . Assignment problems can deal with optimal assignment of teams of collaborating agents ; planning problems can be effects-based planning that search for promising plans to get desired end states with minimal cost ; rescheduling problems can be multi-criteria optimization of rescheduling resources that modify existing original schedule .
Vehicle Routing Problem being a combinatorial class of problems has implications in various areas and applications where traditional methods to find a search space either fail or slow down especially in case of real time systems . Using the most popular heuristic searching approach , Genetic Algorithm , there have been solutions in the state of the art to the problems like Vehicle Routing , Traveling Sales Person , etc .
Today , finding a viable solution for any real world problem focusing on combinatorial of problems is a crucial task . However , using optimisation techniques , a viable best solution for a specific problem can be obtained , developed and solved despite the existing limitations of the implemented technique .
Abstract This paper presents the main results obtained in the field of approximation algorithms in a unified framework . Most of these results have been revisited in order to emphasize two basic tools useful for characterizing approximation classes , that is , combinatorial properties of problems and approximation preserving reducibilities . In particular , after reviewing the most important combinatorial characterizations of the classes PTAS and FPTAS , we concentrate on the class APX and , as a concluding result , we show that this class coincides with the class of optimization problems which are reducible to the maximum satisfiability problem with respect to a polynomial-time approximation preserving reducibility .
The tasks include : the automated creation of database , the rapid query of catalogs , cross-match query and the visualization of the queried results . Especially for cross-matching service , many choices are provided for users , such as one-to-one entry , one-to-many entry , one-to-none entry , none-to-one entry . Meanwhile , the probability of cross-matching is given .
This way we expect substantial benefits in computing capabilities in data analysis . As a first development using our solution , we propose a cross-matching service for the Taiwan Extragalactic Astronomical Data Center .
In today 's global village , it is critical that the key information tools , such as web search engines , e-Commerce portals and e-Governance , work across multiple natural languages , seamlessly We propose a new flexible architecture – Multilingual Information processing on Relational Architecture ( MIRA ) – that supports the multilingual processing functionality of the primary storage mechanism for such deployments – the relational database systems , effectively and efficiently We propose new linguistic matching operators that enhances the standard lexicographic matching of database systems into phonetic and semantic domains We further show that the performance of the systems may be made language-neutral Our proposed architecture is based on standards and hence amenable for easy implementation in any type of query processing and information retrieval systems In this paper , we present our approach to implement the above architecture and outline the host of research issues that are opened up due to the inherently fuzzy nature of the alternative matching semantics .
The modeling of text queries as sequences of embeddings for conducting similarity matching based search within speech features has been recently shown to improve keyword search ( KWS ) performance , especially for the out-of-vocabulary ( OOV ) terms . This technique uses a dynamic time warping based search methodology , converting the KWS problem into a pattern search problem by artificially modeling the text queries as pronunciation-based embedding sequences .
Firstly , the implementing framework of SDCM is constructed . Then , combined with the theories of ontology and dynamic description logic , a semantics-based SDCM algorithm is designed , which includes four implementation stages , respectively , basic information matching , IOPE parameters ( Input , Outputs , Preconditions , Effects ) matching , QoS ( Quality of Service ) matching and comprehensive matching . Finally , a case verifies the feasibility and effectiveness of the proposed method .
The decision where to run which part of the application has to take into account the current latencies to the remote resources and the dynamics of their availability and usage . This requires a Resource Management System that is able to discover matching remote resources fast and efficiently without delaying the offloading procedure too much . Subsequently , we have to determine when or under which conditions which parts shall be offloaded .
The invention discloses a sensor code matching processing method and device , a network platform device , and a gateway of the internet of things . The method includes the following steps of ; receiving a code matching application message for requesting code matching of a sensor , wherein the code matching application message carries identification information for identifying the sensor ; determining a code matching state of the sensor according to the identification information ; and sending the determined code matching state to the gateway of the internet of things , wherein the code matching state is used for that the gateway of the internet of things performs code matching processing on the sensor . According to the method , the problem in correlation techniques that potential risk exists in the process of code matching of the sensor due to illegal intruders can be solved , so that the security of sensor access can be effectively improved .
Windowed subsequence matching over deterministic strings has been studied in previous work in the contexts of knowledge discovery , data mining , and molecular biology . However , we observe that in these applications , as well as in data stream monitoring , complex event processing , and time series data processing in which streams can be mapped to strings , the strings are often noisy and probabilistic .
Our system uses STIX to express threat information , including CoAs . We identified the problem of matching CoAs with the actions that a response system can carry out as a major design challenge , and found a robust and scalable decentralized solution to that challenge by adopting a publish-subscribe model . We built a solution based on the Extensible Messaging and Presence Protocol ( XMPP ) architecture and communications protocol as it provided the right security properties as well as the needed extensibility for both data model and transport protocols .
The enhanced eText is also available to buy separately and works on desktops and tablets . Follows the organizational structure of the new Biology guide , with a focus on the Essential Ideas , Understanding , Applications & Skills for complete syllabus-matching . Written by the highly experienced IB author team of Alan Damon , Randy McGonegal , Patricia Tosto and William Ward , you can be confident that you and your students have all the resources you will need for the new Biology curriculum .
Given an undirected/directed large weighted data graph and a similar smaller weighted pattern graph , the problem of weighted subgraph matching is to find a mapping of the nodes in the pattern graph to a subset of nodes in the data graph such that the sum of edge weight differences is minimum . Biological interaction networks such as protein-protein interaction networks and molecular pathways are often modeled as weighted graphs in order to account for the high false positive rate occurring intrinsically during the detection process of the interactions .
We have explored other design choice of the distance metric . We conduct experiments using discriminator feature distance in our regularization term in a way similar to feature matching loss , where denotes the layer of the discriminator . We apply it to Pix2Pix on the facades dataset .
We note that a similar loss is used in VAE - GANs . Our full objective combines both GAN loss and feature matching loss as : where controls the importance of the two terms . Note that for the feature matching loss , only serves as a feature extractor and does not maximize the loss .
Image - space loss . Our next baseline controls for the feature matching loss used to train the CRN . Here we use exactly the same architecture as in Section 3 , but use only the first layer Φ 0 ( image color ) in the loss :
As a tradeoff , Fischer and Ilg perform feature matching only at a reduced spatial resolution . We reduce the computational burden of feature matching by using a short - ranged matching of warped CNN features at sampled positions and a sub - pixel refinement at every pyramid level . We are inspired by the feature transformation used in Spatial Transformer .
instance - level model alignments for 6D object pose estimation as well as to find surface correspondences in 3D meshes . To facilitate further research in the area of 3D keypoint matching and geometric registration , we provide a correspondence matching benchmark as well as a surface registration benchmark similar to [ reference ] , but with real - world scan data . section : Related Work
We find keypoints whose descriptors are mutually closest to each other in Euclidean space , and use RANSAC over the 3D positions of these keypoint matches to estimate a rigid transformation between the two point clouds . section : Matching Local Geometry in Scenes We evaluate our 3DMatch - based geometric registration algorithm ( i.e. , 3DMatch + RANSAC ) on both real and synthetic datasets .
Our best score on this task is 87.3 % accuracy obtained with the full tree matching NTI model . The previous best performing model on the task performs phrase matching by using the attention mechanism . Our results show that NTI - SLSTM improved the performance of the sequential LSTM encoder by approximately 2 % .
We use new techniques to find new bounds , narrow some of the gaps between the lower bounds and upper bounds and find new deterministic and randomized algorithms with small query and time complexities . Some of the bounds are tight in the sense that finding better ones either gives a breakthrough result in some long-standing combinatorial open problem or needs a new technique that is beyond the existing ones .
We propose the online-decomposition algorithm ( ODA ) based on offline preprocessing that optimizes each static problem component for discretized values of shared constraint variables , and approximate the optimal aggregated utility functions . ODA uses the pre-processed approximated aggregated cost functions to decompose the original problem into smaller problems , and utilizes search heuristics for the combinatorial part of the problem based on the pre-processed look-up tables . We also conduct an initial experimental evaluation which shows that ODA , as compared with MILP , provides an order of magnitude improvement in terms of both computational time and the quality of found solutions for a class of problems for which preprocessing is possible .
The composition model allows set union and difference among the selected elements , while regularizing the problem by restricting their count to a fixed level . This is a combinatorial problem addressing which is not in general computationally tractable . Convex cardinal shape composition ( CSC ) is a recent relaxation scheme presented as a proxy to the original problem .
Search heuristics and the influence of non-perfect randomness: examining Genetic Algorithms and Simulated Annealing Convergence Results for Vehicle Routing Problems with Stochastic Demands A Parameter Tuning Methodology for Metaheuristics Based on Design of Experiments Two-Stage Ant Colony Optimization for Solving the Traveling Salesman Problem Cloud Based Software Computing as a Service in Hybrid Evolution Algorithm with Feedback Assistance Cloud Feedback Assistance Based Hybrid Evolution Algorithm for Optimal Data Solution Avoiding local optima in thep-hub location problem using tabu search and GRASP Phase Transitions in Machine Learning On Dynamic Phase Transitions in Satisfiability Problems A Reinforcement Learning Hyper-heuristic for the optimisation of Flight Connections A Review of the Application of Meta-Heuristic Algorithms to 2D Strip Packing Problems Evaluating the Difficulty of Instances of the Travelling Salesman Problem in the Nearby of the Optimal Solution Based on Random Walk Exploration A decision support system for managing combinatorial problems in container terminals Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization 10, 000+ Times Accelerated Robust Subset Selection (ARSS) : Compiling problem specifications into SAT A Set-based Scheduling Problem for Square Rack Automated Storage and Retrieval Systems Traffic-Aware Energy-Saving Base Station Sleeping and Clustering in Cooperative Networks Complexity of Optimal Lobbying in Threshold Aggregation Optimal Input Elimination Implementation of Distributed Multi-Agent Scheduling Algorithm Based on Pi-calculus Improving heterogeneous system efficiency: architecture, scheduling, and machine learning Scheduling and Replacement Policies for a Hierarchical Multimedia Storage Server Designing a Hierarchical Multimedia Storage Server Energy conscious multi-site computation offloading for mobile cloud computing Dynamic Resource Reservation Based Collision and Deadlock Prevention for Multi-AGVs Power-aware broadcasting and activity scheduling in ad hoc wireless networks using connected dominating sets Transaction processing in mobile database system An Application-Level Service Control Mechanism for QoS-Based Grid Scheduling A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System A swarm intelligent method for traffic light scheduling: application to real urban traffic networks Reservation-Based Federated Scheduling for Parallel Real-Time Tasks Fixed route transit scheduling in Florida: The state of the industry Communication complexity of approximate maximum matching in the message-passing model Complexity and efficient approximability of two dimensional periodically specified problems A high-performance framework for analyzing massive complex networks Periodically Specified Problems : An Exponential Complexity Gap between Exact and Approximate Solutions Neural network algorithms for hypergraph optimization problems Using Genetic Algorithms for Investigating Specific Regions of the Solution Space An Improved Genetic Algorithm Based Solution to Vehicle Routing Problem over OpenMP with Load Consideration Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017) Approximate Solution of NP Optimization Problems A system integrated with query, cross-matching, and visualization Development of an Astrophysical Specific Language for Big Data Computation MIRA: Multilingual Information Processing on Relational Architecture Generative RNNs for OOV Keyword Search A classification matching method for manufacturing resource in cloud manufacturing environment MOCCAA - MObile Cloud Computing AdaptAble Sensor code matching processing method and device, network platform device, and gateway of internet of things Online windowed subsequence matching over probabilistic sequences An Actionable Threat Intelligence system using a Publish-Subscribe communications model Biology : supporting every leaner across the IB continuum WSM: a novel algorithm for subgraph matching in large weighted graphs Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Photographic Image Synthesis with Cascaded Refinement Networks LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Neural Tree Indexers for Text Understanding Exact learning of juntas from membership queries Online Optimization through Preprocessing for Multi-stage Production Decision Guidance Queries Object Learning and Convex Cardinal Shape Composition
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IWINAC (2) ITCS Encyclopedia of Machine Learning and Data Mining HAIS AAAI 2015 AAMAS SmartCom OTM Conferences (2) EDBT Workshops SIGMOD Conference WISCS@CCS 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) EACL ICDE Workshops
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