An adaptive clonal selection algorithm with multiple differential evolution strategies

Clonal selection algorithms have provided significant insights into numerical optimization problems. However, most mutation operators in conventional clonal selection algorithms have semi-blindness and lack an effective guidance mechanism, which has thus become one of the important factors restricting the performance of algorithms. To address these problems, this study develops an improved clonal selection algorithm called an adaptive clonal selection algorithm with multiple differential evolution strategies (ADECSA) with three features: (1) an adaptive mutation strategy pool based on its historical records of success is introduced to guide the immune response process effectively; (2) an adaptive population resizing method is adopted to speed up convergence; and (3) a premature convergence detection method and a stagnation detection method are proposed to alleviate premature convergence and stagnation problems in the evolution by enhancing the diversity of the population. Experimental results on a wide variety of benchmark functions demonstrate that our proposed method achieves better performance than both state-ofthe-art clonal selection algorithms and differential evolution algorithms. Especially in the comparisons with other clonal selection algorithms, our proposed method outperforms at least 23 out of 30 benchmark functions from the CEC2014 test suite. (c) 2022 Elsevier Inc. All rights reserved.