Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition

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dc.contributor.authorFranz, Wayne
dc.contributor.examiningcommitteeDomaratzki, Michael (Computer Science) Ferens, Ken (Electrical and Computer Engineering)en_US
dc.contributor.supervisorThulasiraman, Parimala (Computer Science)en_US
dc.date.accessioned2014-08-21T21:02:58Z
dc.date.available2014-08-21T21:02:58Z
dc.date.issued2013en_US
dc.date.issued2013en_US
dc.date.issued2014en_US
dc.date.issued2014en_US
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractRecent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.en_US
dc.description.noteOctober 2014en_US
dc.identifier.citationOptimization of an OpenCL-Based Multi-Swarm PSO Algorithm on an APU. The 10th International Conference on Parallel Processing and Applied Mathematics, Warsaw, Poland, Sept. 8-11, 2013, Lecture Notes in Computer Science, Springer.en_US
dc.identifier.citationMemory Efficient Multi-Swarm PSO Algorithm in OpenCL on an APU. The 13th International Conference on Algorithms and Architectures for Parallel Processing, Vietri sul Mare, Italy, Dec. 18-20, 2013, Lecture Notes in Computer Science, Springer.en_US
dc.identifier.citationEffect of Communication Topologies on Hybrid Evolutionary Algorithms. The 6th World Congress on Nature and Biologically Inspired Computing, Porto, Portugul, Jul. 30-Aug. 1, 2014.en_US
dc.identifier.citationExploration/Exploitation of a Hybrid Enhanced MPSO-GA Algorithm on a Fused CPU-GPU Architecture. Concurrency and Computation: Practice and Experience, John Wiley & Sons Inc., 2014.en_US
dc.identifier.urihttp://hdl.handle.net/1993/23842
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.publisherSpringeren_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.publisherJohn Wiley & Sonsen_US
dc.rightsopen accessen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectGenetic Algorithmen_US
dc.subjectGPUen_US
dc.subjectMetaheuristicen_US
dc.titleMulti-population PSO-GA hybrid techniques: integration, topologies, and parallel compositionen_US
dc.typemaster thesisen_US
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