A hybrid clonal selection algorithm with modified combinatorial recombination and success-history based adaptive mutation for numerical optimization

摘要

Artificial immune system is a class of computational intelligence methods drawing inspiration from biological immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. When dealing with complex optimization problems, such as the characteristics of multimodal, high-dimension, rotational, the traditional CSA often suffers from diversity loss, poor search ability, premature convergence and stagnation. To address the problems, a modified combinatorial recombination is introduced to bring diversity to the population and avoid the premature convergence. Moreover, the success-history based adaptive mutation strategy is introduced to form a success-history based adaptive mutation based clonal selection algorithm to improve the search ability. The mutation operator is also modified and analyzed through experimental comparison. To further improve the precision and cope with the stagnation, the gene knockout strategy is proposed. The proposed algorithm is tested on CEC 2014 benchmarks and compared with state-of-the-art evolutionary algorithms. The experimental results show that MSHCSA is quite competitive.