摘要
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。
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.
出处
《通信学报》
EI
CSCD
北大核心
2015年第7期166-175,共10页
Journal on Communications
基金
国家自然科学基金资助项目(61273232
41101425)
教育部新世纪优秀人才支持计划基金资助项目(NECT-2013-0785)~~
关键词
进化计算
动态优化
自学习机制
多中心动态优化问题
差异进化
evolutionary computation
dynamic optimization
self-learning mechanism
multi-center dynamic optimization problems
differential evolution
