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An Improved Clonal Algorithm in Multiobjective Optimization

Author(s): Jianyong Chen | Qiuzhen Lin | Qingbin Hu

Journal: Journal of Software
ISSN 1796-217X

Volume: 4;
Issue: 9;
Start page: 976;
Date: 2009;
Original page

Keywords: multiobjective optimization | immune algorithm | clonal selection | hybrid mutation

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.
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