Enhanced Particle Swarm Optimization with Self-Adaptation on Entropy-Based Inertia Weight

Hei-Chia WANG  Che-Tsung YANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.2   pp.324-331
Publication Date: 2016/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7304
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
adaptive inertia weight,  exploration-exploitation trade-off,  particle swarm optimization,  entropy,  

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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.