Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

Authors

  • K. M. Ang Faculty of Engineering, Technology and Built Environment, UCSI University, 56000 Kuala Lumpur, Malaysia.
  • Z. S. Yeap Faculty of Engineering, Technology and Built Environment, UCSI University, 56000 Kuala Lumpur, Malaysia
  • C. E. Chow Faculty of Engineering, Technology and Built Environment, UCSI University, 56000 Kuala Lumpur, Malaysia
  • W. Cheng Faculty of Engineering, Technology and Built Environment, UCSI University, 56000 Kuala Lumpur, Malaysia
  • W. H. Lim Faculty of Engineering, Technology and Built Environment, UCSI University, 56000 Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.15282/mekatronika.v3i1.7150

Keywords:

Particle swarm optimization, Modified initialization scheme, Chaotic, Oppositional-based learning, Metaheuristic search algorithm

Abstract

Different variants of particle swarm optimization (PSO) algorithms were introduced in recent years with various improvements to tackle different types of optimization problems more robustly. However, the conventional initialization scheme tends to generate an initial population with relatively inferior solution due to the random guess mechanism. In this paper, a PSO variant known as modified PSO with chaotic initialization scheme is introduced to solve unconstrained global optimization problems more effectively, by generating a more promising initial population. Experimental studies are conducted to assess and compare the optimization performance of the proposed algorithm with four existing well-establised PSO variants using seven test functions. The proposed algorithm is observed to outperform its competitors in solving the selected test problems.

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Published

2021-06-19

How to Cite

[1]
K. M. . Ang, Z. S. Yeap, C. E. Chow, W. Cheng, and W. H. Lim, “Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems”, Mekatronika: J. Intell. Manuf. Mechatron., vol. 3, no. 1, pp. 35–43, Jun. 2021.

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Section

Original Article