Volume 4 Number 9 (Nov. 2009)
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JSW 2009 Vol.4(9): 976-983 ISSN: 1796-217X
doi: 10.4304//jsw.4.9.976-983

An Improved Clonal Algorithm in Multiobjective Optimization

Jianyong Chen, Qiuzhen Lin and Qingbin Hu
Department of Computer Science and Technology, Shenzhen University, Shenzhen, P.R. China

Abstract—In this paper, we develop a novel clonal algorithm for multiobjective optimization (NCMO) which is improved from three approaches, i.e., dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM operator). Among them, the GP-HM operator is controlled by the dynamic mutation probability. These approaches adopt a cooling schedule, reducing the parameters gradually to a minimal threshold. By this means, they can enhance exploratory capabilities, and keep a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front. When comparing NCMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that NCMO evidently has better performance.

Index Terms—multiobjective optimization; immune algorithm; clonal selection; hybrid mutation


Cite: Jianyong Chen, Qiuzhen Lin and Qingbin Hu, "An Improved Clonal Algorithm in Multiobjective Optimization," Journal of Software vol. 4, no. 9, pp. 976-983, 2009.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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