AMT-PSO: An Adaptive Magnification Transformation Based Particle Swarm Optimizer

Junqi ZHANG  Lina NI  Chen XIE  Ying TAN  Zheng TANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D    No.4    pp.786-797
Publication Date: 2011/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.786
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
particle swarm optimizer,  magnification transformation,  exploitation,  exploration,  search strategy,  adaptive,  

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This paper presents an adaptive magnification transformation based particle swarm optimizer (AMT-PSO) that provides an adaptive search strategy for each particle along the search process. Magnification transformation is a simple but very powerful mechanism, which is inspired by using a convex lens to see things much clearer. The essence of this transformation is to set a magnifier around an area we are interested in, so that we could inspect the area of interest more carefully and precisely. An evolutionary factor, which utilizes the information of population distribution in particle swarm, is used as an index to adaptively tune the magnification scale factor for each particle in each dimension. Furthermore, a perturbation-based elitist learning strategy is utilized to help the swarm's best particle to escape the local optimum and explore the potential better space. The AMT-PSO is evaluated on 15 unimodal and multimodal benchmark functions. The effects of the adaptive magnification transformation mechanism and the elitist learning strategy in AMT-PSO are studied. Results show that the adaptive magnification transformation mechanism provides the main contribution to the proposed AMT-PSO in terms of convergence speed and solution accuracy on four categories of benchmark test functions.