Adaptive Bare Bones Particle Swarm Inspired by Cloud Model

Junqi ZHANG  Lina NI  Jing YAO  Wei WANG  Zheng TANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D    No.8    pp.1527-1538
Publication Date: 2011/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1527
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
particle swarm optimizer,  hybrid uniform distribution,  exploitation,  exploration,  search strategy,  

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Kennedy has proposed the bare bones particle swarm (BBPS) by the elimination of the velocity formula and its replacement by the Gaussian sampling strategy without parameter tuning. However, a delicate balance between exploitation and exploration is the key to the success of an optimizer. This paper firstly analyzes the sampling distribution in BBPS, based on which we propose an adaptive BBPS inspired by the cloud model (ACM-BBPS). The cloud model adaptively produces a different standard deviation of the Gaussian sampling for each particle according to the evolutionary state in the swarm, which provides an adaptive balance between exploitation and exploration on different objective functions. Meanwhile, the diversity of the swarms is further enhanced by the randomness of the cloud model itself. Experimental results show that the proposed ACM-BBPS achieves faster convergence speed and more accurate solutions than five other contenders on twenty-five unimodal, basic multimodal, extended multimodal and hybrid composition benchmark functions. The diversity enhancement by the randomness in the cloud model itself is also illustrated.