Abstract: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.