混沌反向学习和声搜索算法

为改善和声搜索算法易陷入局部最优的不足,提出了一种混沌反向学习和声搜索(COLHS)算法.基于聚集和发散思想,对算法陷入局部最优和停滞状态进行初步预判断,并根据预判断的结果融合混沌扰动策略和反向学习,利用了logistic混沌序列的遍历性和反向学习的空间可扩展性.此外,利用和声记忆库的历史信息定义更新因子和进化因子,自适应地调整参数基音调整概率(PAR)和基音调整步长(BW),平衡算法的聚集和发散.数值结果表明,COLHS算法优于HS算法及最近文献报道的8种改进的HS算法.

Chaos Opposition Based Learning Harmony Search Algorithm

Harmony search (HS) algorithm is easily trapped into local optimal. To improve this shortcoming， chaos opposition based learning harmony search (COLHS) algorithm was proposed. Based on the thought of aggregation and divergence， preliminary judgments whether this algorithm was trapped into local optimal or backwater status were given， then according to the judge result， disturbance strategy was integrated with opposition based learning technology. The ergodicity of logistic chaos sequence and the space extensibility of opposition based learning were used. Besides， to balance aggregation and divergence， the history information of harmony memory was used to define the updating factor and the evolution factor， which were applied to dynamically adjust the pitch adjustment rate (PAR) and the bandwidth (BW). Numerical results demonstrated that the proposed algorithm is better than HS and the other eight kinds of improved HS algorithms that reported in recent literatures.