Original scientific paper

https://doi.org/10.17559/TV-20170413135109

Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

Shafi Ullah Khan orcid.org/0000-0002-5706-721X ; Department of Electronics Islamia College University, Peshawar, Pakistan
Obaid Ur Rehman orcid.org/0000-0003-4577-6059 ; Sarhad University of Science & Information Technology, Peshawar, Pakistan
Naeem Khan orcid.org/0000-0002-7169-6733 ; Depertment of Electrical Engineering, University of Engineering & Technology, Bannu Campus, Pakistan
Asfandyar Khan ; Department of CS/IT University of Agriculture, Peshawar, Pakistan
Syed Anayat Ali Shah ; Department of Mathimatics, Islamia College University, Peshawar, Pakistan
Shiyou Yang ; College Electrical Engineering, Zhejiang University, Hangzhou, China

Abstract

Particle swarm optimization is a stochastic optimal search algorithm inspired by observing schools of fishes and flocks of birds. It is prevalent due to its easy implementation and fast convergence. However, PSO has been known to succumb to local optima when dealing with complex and higher dimensional optimization problems. To handle the problem of premutature convergence in PSO, this paper presents a novel adaptive inertia weight strategy and modifies the velocity update equation with the new Sbest term. To maintain the diversity of the population a particular radius r is introduced to impulse cluster particles. To validate the effectiveness of the proposed algorithm, various test functions and typical engineering applications are employed, and the experimental results show that with the changing of the proposed parameter the performance of PSO improves when dealing with these complex and high dimensional problems.

Keywords

adaptive inertia weight; global optimization; PSO; radius r, S best particle; Team 22

Hrčak ID:

212815

URI

https://hrcak.srce.hr/212815

Publication date:

16.12.2018.

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