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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/10509
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dc.contributor.authorPant M.-
dc.contributor.authorThangaraj R.-
dc.contributor.authorAbraham A.-
dc.contributor.authorDeep, K.-
dc.date.accessioned2020-10-15T12:06:30Z-
dc.date.available2020-10-15T12:06:30Z-
dc.date.issued2009-
dc.identifier.citationInternational Journal of Simulation: Systems, Science and Technology (2009), 10(3): 89-98-
dc.identifier.issn14738031-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/10509-
dc.description.abstractParticle Swarm Optimization (PSO) is an evolutionary computation technique based on Swarm Intelligence. The PSO algorithm is simple in concept, easy to implement and computationally efficient. Many researchers have worked on improving its performance in various ways and have developed several interesting variants. In this paper, we present a new mutation operator called the Systematic Mutation (SM) operator for enhancing the performance of Basic Particle Swarm Optimization (BPSO) algorithm. The SM operator unlike most of its contemporary mutation operators do not use the random probability distribution for perturbing the swarm population, but uses a quasi random Sobol sequence to find new solution vectors in the search domain. The presence of SM operator makes the mutated particles to move systematically in the search space. The comparison of SMPSO is made with BPSO and some other variants of PSO on a set of 15 benchmark global optimization problems and three real life engineering design problems. The empirical results show that SM operator significantly improves the performance of PSO in terms of fitness function value.-
dc.language.isoen_US-
dc.relation.ispartofInternational Journal of Simulation: Systems, Science and Technology-
dc.subjectLow discrepancy sequence-
dc.subjectMutatation-
dc.subjectParticle Swarm Optimization-
dc.subjectSobol sequence-
dc.titleParticle Swarm Optimization using Sobol mutation-
dc.typeArticle-
dc.scopusid23467551900-
dc.scopusid24345289500-
dc.scopusid7202760099-
dc.scopusid8561208900-
dc.affiliationPant, M., Indian Institute of Technology Roorkee, India-
dc.affiliationThangaraj, R., Indian Institute of Technology Roorkee, India-
dc.affiliationAbraham, A., Machine Intelligence Research (MIR) Labs, Scientific Network for Innovation and Research Excellence (SNIRE), United States-
dc.affiliationDeep, K., Indian Institute of Technology Roorkee, India-
dc.description.correspondingauthorPant, M.; Indian Institute of Technology RoorkeeIndia; email: millifpt@iitr.ernet.in-
Appears in Collections:Journal Publications [MA]

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