Enhanced particle swarm optimization with self-adaptation on entropy-based inertia weight

Hei Chia Wang, Che Tsung Yang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.

Original languageEnglish
Pages (from-to)324-331
Number of pages8
JournalIEICE Transactions on Information and Systems
Issue number2
Publication statusPublished - 2016 Feb

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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