基于感知角色和多发现者的动态群搜索优化算法

Sensitive Individuals and Multi-Producer Based Dynamic GSO

针对基本群搜索算法(GSO)不能及时适应动态环境变化、容易陷入局部极值的问题,提出一种基于感知者角色和多发现者的动态群搜索算法(SMGSO).引入"感知者"角色用以检测环境变化,重新初始化一定比例的种群个体以响应环境变化;采用多发现者模式,提出了基于多发现者中心的加入者更新模式,以提高搜索精度;采用基于群体多样性的角色分配策略,确定加入者和游荡者的比例与数量,提高种群多样性.实验结果表明,在解决动态寻优问题时,SMGSO算法表现出更好的性能,能够更准确、更及时地跟踪动态目标.

Failing to adapt to dynamic changes and depart fromlocal optima are two disadvantages of basic group search optimizer （GSO） in the dynamic environment. A sensitive individuals and multi-producer based dynamic GSO named SMGSO is proposed in this paper for dynamic optimization problems. Firstly, sensitive individuals are introduced in GSO in addition to producer, scroungers and rangers, which are responsible for detecting the environmental change. If environmental changes are detected, some individuals are initialized to respond to them. Secondly, a new update model of scroungers is proposed based on the center of multi-producer to improve local search ability. At last, arole assignment strategy based on population diversitywhichis beneficial for keep stable diversity is adopted to determine the ratio of scroungers to rangers. Experimental results demonstrate that SMGSO is superior to other heuristic algorithms in dynamic environment, which may not only find the optima as possibleas closely but also trackthe changed optimatimely.