解决动态多中心问题的自学习差异进化算法

为解决动态环境下的多中心优化问题，提出自学习差异进化算法。通过评估特定个体检测到环境变化，自学习算子将群体引至新的环境，并保持群体的拓扑结构不变，以继续当前的进化趋势。采用邻域搜索机制加快算法的收敛速度，引入随机个体迁入机制增加群体多样性。实验以周期动态函数为测试对象，比较自学习差异进化算法与部分智能优化算法的性能，结果表明，新算法有更快的收敛速度和更好的环境适应能力。

Self-learning differential evolution algorithm for dynamic polycentric problems

A novel self-learning differential evolution algorithm is proposed to solve dynamical multi-center optimization problems. The approach of re-evaluating some specific individuals is used to monitor environmental changes. The proposed self-learning operator guides the evolutionary group to a new environment, meanwhile maintains the stable topology structure of group to maintain the current evolutionary trend. A neighborhood search mechanism and a random immigrant mechanism are adapted to make a tradeoff between algorithmic convergence and population diversity. The experiment studies on a periodic dynamic function set suits are done, and the comparisons with peer algorithms show that the self-learning differential algorithm outperforms other algorithms in term of convergence and adaptability under dynamical environment.