APPLICATION OF NOVEL CLONAL ALGORITHM IN MULTIOBJECTIVE OPTIMIZATION

Jianyong Chen (), Qiuzhen Lin () and Qingbin Hu ()
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Jianyong Chen: College of Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen, Guangdong 518060, P. R. China
Qiuzhen Lin: College of Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen, Guangdong 518060, P. R. China
Qingbin Hu: College of Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen, Guangdong 518060, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2010, vol. 09, issue 02, 239-266

Abstract: In this paper, a novel clonal algorithm applied in multiobjecitve optimization (NCMO) is presented, which is designed from the improvement of search operators, 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. The main notion of these approaches is to perform more coarse-grained search at initial stage in order to speed up the convergence toward the Pareto-optimal front. Once the solutions are getting close to the Pareto-optimal front, more fine-grained search is performed in order to reduce the gaps between the solutions and the Pareto-optimal front. Based on this purpose, a cooling schedule is adopted in these approaches, reducing the parameters gradually to a minimal threshold, the aim of which is to keep a desirable balance between fine-grained search and coarse-grained search. By this means, the exploratory capabilities of NCMO are enhanced. When compared with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that NCMO has remarkable performance.

Keywords: Multiobjective optimization; immune algorithm; clonal selection; hybrid mutation (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1142/S0219622010003804

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