用于全局优化问题的混合免疫进化算法

doi:10.3969/j.issn.1001-2400.2010.05.034

Abstract

Abstract:

In order to overcome the premature of the immune algorithm when solving high dimensional multimodal functions, an efficient hybrid immune evolutionary algorithm is proposed. The main characteristics of the novel hybrid algorithm are dynamic clonal selection, learning-based hypermutation and multi-parentic crossing operators. In addition, a novel performance evaluation criterion for comparing different algorithms is constructed. In an experimental study, firstly the performance of the proposed HIEA is validated using several classical test functions; next HIEA is compared with self-adaptive differential evolution (SaDE) and a simple immune algorithm (SIA) under a certain amount of function evaluations, experimental results show that the performance of the proposed HIEA is significantly better than that of SaDE and SIA in terms of accuracy and stability.

Key words: global optimization problems, artificial immune systems, clonal selection algorithm, multi-parentic crossing operator

LIU Xing-bao;CAI Zi-xing;WANG Yong;PENG Wei-xiong. Hybrid immune evolutionary algorithm for global optimization problems[J].J4, 2010, 37(5): 971-980.

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Hybrid immune evolutionary algorithm for global optimization problems

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