Department of Computer Science and Technology, Shenzhen University, Shenzhen, P.R. China

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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

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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.