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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 6519678, 19 pages
Research Article

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

1Department of Electrical Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 320, Taiwan
2Center for Research & Development and Department of Electronics Engineering, Adamson University, 1000 Manila, Philippines
3School of Graduate Studies, Mapua Institute of Technology, 1002 Manila, Philippines
4School of Electrical Electronics Computer Engineering, Mapua Institute of Technology, 1002 Manila, Philippines

Received 11 April 2016; Accepted 5 July 2016

Academic Editor: Zhen-Lai Han

Copyright © 2016 Ying-Yi Hong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.