An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Adaptive Local Search

This paper demonstrates a novel local search approach based on an adaptive (time variant) search space index improving the exploration ability as well as diversity in multi-objective Particle Swarm Optimization. The novel strategy searches for the neighbourhood particles in a range which gradually i...

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Vydané v:International journal of applied evolutionary computation Ročník 8; číslo 2; s. 1 - 29
Hlavní autori: Kapse, Swapnil Prakash, Krishnapillai, Shankar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hershey IGI Global 01.04.2017
Predmet:
Adaptive algorithms
Algorithms
Analysis
Archives
Archives & records
Archiving
Convergence
Genetic algorithms
Mathematical optimization
Mechanical engineering
Multiple objective analysis
Mutation
Optimization algorithms
Particle swarm optimization
Searching
Strategy
Swarm intelligence
Velocity
India
ISSN:1942-3594, 1942-3608
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Shrnutí:This paper demonstrates a novel local search approach based on an adaptive (time variant) search space index improving the exploration ability as well as diversity in multi-objective Particle Swarm Optimization. The novel strategy searches for the neighbourhood particles in a range which gradually increases with iterations. Particles get updated according to the rules of basic PSO and the non-dominated particles are subjected to Evolutionary update archiving. To improve the diversity, the archive is truncated based on crowding distance parameter. The leader is chosen among the candidates in the archive based on another local search. From the simulation results, it is clear that the implementation of the new scheme results in better convergence and diversity as compared to NSGA-II, CMPSO, and SMPSO reported in literature. Finally, the proposed algorithm is used to solve machine design based engineering problems from literature and compared with existing algorithms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1942-3594
1942-3608
DOI:10.4018/IJAEC.2017040101