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

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  • Swapnil Prakash Kapse

    (Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India)

  • Shankar Krishnapillai

    (Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India)

Abstract

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.

Suggested Citation

  • Swapnil Prakash Kapse & Shankar Krishnapillai, 2017. "An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Adaptive Local Search," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 8(2), pages 1-29, April.
    • Handle: RePEc:igg:jaec00:v:8:y:2017:i:2:p:1-29
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