Aiming at the shortcomings of clonal selection algorithm(CSA)in solving complex optimization problems,such as low efficiency,slow convergence speed and easy to fall into local optimum,an improved clonal selection algorithm based on multi-strategy(MSICSA)was proposed.Firstly,the Sobol sequence was introduced to initialize the population,which enriched the diversity of the population and improved the overall stability of the algorithm.Secondly,the sine cosine optimization strategy was introduced to enhance the global search ability of the algorithm to avoid falling into local optimum and causing the stagnation of the algorithm.Finally,a dynamic adaptive concentration adjustment strategy was introduced to adjust the antibody concentration in the search space at different periods of the algorithm,which strengthened the global search ability in the early stage and the local optimization ability in the later stage,and improved the convergence speed of the algorithm.The ablation experiment shows the effectiveness of the improved strategy,and the perturbation experiment verifies the stability and robustness of the proposed algorithm.The comparative simulation show that MSICSA can effectively improve the convergence speed and optimization accuracy,and improve the ability to jump out of local optimum.
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维普
