Performance analysis of coarse-grained parallel genetic algorithms on the multi-core sun UltraSPARC T1

Byun, Jong-Ho; Datta, Kushal; Ravindran, Arun; Mukherjee, Arindam; Joshi, Bharat

doi:10.1109/secon.2009.5174094

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…From an architecture point of view, these approaches only exploit parallelism in processors, using shared memory for exchanging information and synchronization tasks. There are works that implement multipopulation heuristics based on ant colony (Delisle et al., ; Dongdong et al., ; Li et al., ), particle swarm (Tu and Liang, ), and evolutionary algorithms (Byun et al., ).…”

Section: Parallel Implementations Of Metaheuristicsmentioning

confidence: 99%

A parallel hybrid metaheuristic for bicluster editing

Filho

Júnior

Cabral

et al. 2015

Int Trans Operational Res

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The bicluster editing problem (BEP) consists of editing (adding or removing) the edges of a bipartite graph G in order to transform it into a vertex‐disjoint union of complete bipartite subgraphs, in such a way that the sum of the weights of the edited edges is minimum. In this paper, we propose five parallel strategies for the implementation of a hybrid metaheuristic for the BEP, consisting of a GRASP with VNS as local search. Computational experiments show near‐linear speedups on Linux cluster with 64 processors and better solutions than those of the sequential approach.

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Section: Parallel Implementations Of Metaheuristicsmentioning

confidence: 99%

A parallel hybrid metaheuristic for bicluster editing

Filho

Júnior

Cabral

et al. 2015

Int Trans Operational Res

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“…Emerging technologies are continuously providing new advanced manufacturing processes, and also new challenges. In this context, several problems have recently been tackled with parallel implementations of metaheuristics, including warehouse location and placement problems (Almeida‐Luz, ; Byun et al., ; Homberger, ; Homberger and Gehring, ), steel industry (Zhao et al., ), packing problems (León et al., ; Peng et al., ; Segura et al., ), and assembly line balancing problems (Ozbakir et al., ). Routing, logistics and vehicle planning, a field where sophisticated methods are needed to manage the flow of resources, especially for large and complex problem instances. Parallel metaheuristics have been recently applied to logistic problems (Fang and Wu, ; Li and Bai, ; Liefooghe, ), facility location (Subramanian et al., ; Wang et al., ), site location (Zhao et al., ), vehicle routing (Doerner et al., , ; Ellabib et al., ; Khouadjia et al., ; Lucka and Piecka, ; Yu et al., ), emergency vehicle fleet management (Ibri et al., ), bus network optimization (Yang et al., ), and path planning (Allaire et al., ), among others problems in this area. Scheduling, which is a key class of planning problems to provide a correct service on deciding how to commit resources to a group of tasks.…”

Section: Modern Applications Solved By Parallel Metaheuristicsmentioning

confidence: 99%

“…The master–slave model for parallel metaheuristics has also been implemented in multicore processors, for ACO (Delisle et al., , ; Guo et al., ; López‐Ibánez, ; Tsutsui, ; Tsutsui and Fujimoto, ), EA (Cardenas et al., ), TS and branch and bound (Hung and Chen, ); in these methods, the main advantage is the ability of computing the fitness evaluation in parallel by using several threads. Multicore multipopulation methods have also been proposed for several metaheuristics, such as ACO (Delisle et al., ; Gao et al., ; Li et al., ; Lucka and Piecka, ; Xiong et al., , ), EAs (Byun et al., ; He et al., ; Tsutsui, ), PSO (Tu and Liang, ), and the parallel artificial bee colony algorithm (Narasimhan, ). When using multiple populations, the shared‐memory approach is used to perform the specific operators that communicate and synchronize the populations, allowing an efficient cooperative search.…”

Section: Technologies For Parallel Metaheuristicsmentioning

confidence: 99%

Parallel metaheuristics: recent advances and new trends

Alba

Luque

Nesmachnow

2012

Int Trans Operational Res

268

118

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The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.

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“…The features of many-core processors include suitability for small and medium scale parallelization of from several to several hundreds of nodes, and low-cost compared to massively parallel computing systems. This environment has stimulated research on parallelization of genetic computing on many-core processors [6][7][8][9][10]. Current reports, however, focus on benchmark tests for genetic computing using typical GPUs.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of genetic algorithms for sudoku solution on many-core processors

Sato

Hasegawa

Sato³

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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In this paper, we use the problem of solving Sudoku puzzles to demonstrate the possibility of achieving practical processing time through the use of many-core processors for parallel processing in the application of genetic computation. To increase accuracy, we propose a genetic operation that takes building-block linkage into account. As a parallel processing model for higher performance, we use a multiple-population coarse-grained GA model to counter initial value dependence under the condition of a limited number of individuals. The genetic manipulation is also accelerated by the parallel processing of threads. In an evaluation using even very difficult problems, we show that execution times of several tens of seconds and several seconds can be obtained by parallel processing with the Intel Corei7 and NVIDIA GTX460, respectively, and that a correct solution rate of 100% can be achieved in either case.

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Performance analysis of coarse-grained parallel genetic algorithms on the multi-core sun UltraSPARC T1

Cited by 15 publications

References 4 publications

A parallel hybrid metaheuristic for bicluster editing

A parallel hybrid metaheuristic for bicluster editing

Parallel metaheuristics: recent advances and new trends

Acceleration of genetic algorithms for sudoku solution on many-core processors

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