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A Modified Particle Swarm Optimization with Elite Archive for Typical Multi-Objective Problems

  • Zheng Li
  • Jinlei QinEmail author
Research Paper
  • 8 Downloads
Part of the following topical collections:
  1. Mathematics

Abstract

The solution to multi-objective optimization problems with conflicting objectives is a Pareto-optimal solution set. It is well known that the critical work in multi-objective particle swarm optimization (MOPSO) is to find the global best guides for each particle in order to obtain satisfied Pareto fronts with high diversity. In this paper, a modified version of MOPSO is proposed, where dense and sparse distance are adopted to determine the global best guides, and Pareto archive with size limit is used to store the non-dominated solutions. In addition, a random number is used to judge whether the crowding distance considered or not, and the inertia weight decreases linearly to improve the speed of convergence and avoid precocity. The proposed approach is applied to several well-known benchmark functions, and the experimental results show that the diversity of swarm and distribution of Pareto fronts are well satisfied.

Keywords

Multi-objective optimization Particle swarm optimization External archive Convergence Dense distance Sparse distance 

Notes

Acknowledgements

The research was supported by the Fundamental Research Funds for the Central Universities (2018MS076, 2015MS128) and the Hebei Province Natural Science Fund Program (F2014502081).

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Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.Department of ComputerNorth China Electric Power University (Baoding)BaodingPeople’s Republic of China

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