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

Hei Chia Wang, Che Tsung Yang

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)324-331
頁數8
期刊IEICE Transactions on Information and Systems
E99D
發行號2
DOIs
出版狀態Published - 2016 二月

All Science Journal Classification (ASJC) codes

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

指紋 深入研究「Enhanced particle swarm optimization with self-adaptation on entropy-based inertia weight」主題。共同形成了獨特的指紋。

引用此