基于双重经验结合的自适应差分进化算法OA北大核心CSTPCD

To improve the performance of the traditional differential evolution for solving some complex optimization problems,an adaptive differential evolution based on dual experience combination was proposed.Adaptive differential evolution based on dual experience combination proposed a parameter adaptive mechanism based on the combination of individual experience and collective experience.In this mechanism,each individual adaptively updated its scaling factor and crossover probability by using its own experience and the collective experience of multiple successful individuals.It not only utilized the individual's evolutionary information well but also combined the beneficial information of the collective,which facilitated the generation of good individuals and improved the performance.In addition,a new mutation strategy with external archiving was proposed.In this mutation strategy,a parameter was introduced to adaptively adjust its greediness at different evolutionary stages.This parameter changed dynamically as the number of function evaluations increased,which better balanced exploration and exploitation and improved the performance.The experiments were conducted on the CEC2017 benchmark suite,and adaptive differential evolution based on dual experience combination was compared with several improved differential evolution algorithms.Experimental results show that adaptive differential evolution based on dual experience combination achieves better solution results,and outperforms other comparative algorithms overall.