动态小生境半径两阶段多模态差分进化算法  

Two-stage differential evolution algorithm using dynamic niche radius for multimodal optimization

作　　者：张贵军[1] 陈铭[1] 周晓根 

机构地区：[1]浙江工业大学信息工程学院,杭州310023

出　　处：《控制与决策》2016年第7期1185-1191,共7页Control and Decision

基　　金：国家自然科学基金项目(61075062;61573317);浙江省自然科学基金项目(LY13F030008);浙江省科技厅公益项目(2014C33088);浙江省公益技术研究社会发展重点项目(2015C33001);浙江省重中之重学科开放基金项目(20151108;20151015)

摘　　要：针对多模态优化问题,提出一种动态小生境半径两阶段多模态差分进化算法.基于构象空间退火思想,设计一种两阶段退火策略来动态调整小生境半径,并根据退火过程将整个优化过程分为两个阶段.在第1阶段,通过差分限制变异策略生成高质量的新个体来维持种群的多样性,促进多模收敛;在第2阶段,利用种子邻近变异策略对已探测到的生境高度搜索,加快算法的收敛速度.实验结果表明,所提出算法能够有效实现从全局探测到局部增强的自适应平滑过渡,是一种有效的多模态优化算法.A two-stage differential evolution algorithm using dynamic niche radius is proposed for multimodal optimization,in which a two-stage annealing schedule based on the idea of conformational space annealing is designed to adjust the niche radius dynamically. Meanwhile, the optimization process is divided into two stages according to the annealing process.Thus, at the first stage, a differential vector limited mutation is used to generate the high-quality individuals to keep the population diversity, thereby facilitating multiple convergence. At the second stage, to enhance the convergence speed, a seed neighborhood mutation strategy is used to exploit the niche highly. Experiment results show that, the proposed algorithm can navigate from global exploration to local exploitation adaptively, which is an effective multimodal optimization algorithm.

关 键 词：差分进化 多模态优化 小生境 两阶段优化 构象空间退火 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]