关键词: 云模型, 反向学习, 克隆选择, 云变异, 高维函数优化

Abstract:

In order to further improve the population diversity, global optimization ability and search efficiency of immune clonal algorithm in high-dimensional object optimization, a clonal selection algorithm combining cloud model and reverse learning（CRCSA） is proposed. The concept of cloud model is introduced, and the forward cloud generator is used to generate cloud mutation factors and mutate the cloned population. The reverse learning strategy is used to find the reverse solution of the population before and after mutation, and then realize the selection of population antibody. Finally, the convergence of the algorithm is proved by Markov chain theory. The test results of six groups of high-dimensional functions show that, compared with Differential Genetic Algorithm（DGA）, Immune Genetic Algorithm（IGA） and Adaptive Chaotic clonal Selection Algorithm（ACSA）, the proposed algorithm achieves 100% optimization, and the minimum iterative algebra, convergence algebra and iterative algebra standard deviation are reduced by 33.7%, 19.8% and 29.1%, respectively, which verifies its strong optimization ability, high search efficiency and good stability.

Key words: cloud model, reverse learning, clone selection, cloud mutation, high-dimensional function optimization