关键词: 群智能算法, 群搜索算法, 预选择机制, 倒序变异, 自适应方法

Abstract: To overcome the prematurity of Group Search Optimizer (GSO) and improve its convergence speed, a producer pre-selection mechanism based self-adaptive group search optimizer (PSAGSO) algorithm was proposed. Firstly, the reverse mutation operator and pre-selection mechanism were employed to generate a new producer by producer-scrounger model to guide the search directions of scrounger and effectively maintain the diversity of population. Secondly, a self-adaptive method based on linear decreasing weight was adopted to adjust the proportion of rangers, which is to improve individual vigor of the population and benefit to escape from local optima. Experiments were conducted on a set of 12 benchmark functions. For 30-dimensional function optimization, the test data obtained by the PSAGSO algorithm was better than that in the literature (HE S, WU Q H, SAUNDERS J R. Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 973-990). For 300-dimensional numerical optimization problems, the PSAGSO algorithm exhibited better performance. The experimental result demonstrates that the PSAGSO algorithm improves the group search optimizer, and to some extent it improves the algorithm convergence speed and accuracy.

Key words: swarm intelligence algorithm, Group Search Optimizer (GSO), pre-selection mechanism, reverse mutation, self-adaptive method