Directed searching optimization algorithm for constrained optimization problems

Authors:
Dexuan Zou;Haikuan Liu;Liqun Gao;Steven Li
Affiliations:
School of Electrical Engineering and Automation, Xuzhou Normal University, Xuzhou, Jiangsu 221116, PR China;School of Electrical Engineering and Automation, Xuzhou Normal University, Xuzhou, Jiangsu 221116, PR China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, PR China;Division of Business University of South Australia GPO Box 2471, Adelaide, SA 5001, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations - position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems.