应用反向学习策略的群搜索优化算法

Group Search Optimizer Applying Opposition-based Learning

群搜索优化算法(Group Search Optimizer,GSO)是一类基于发现者-加入者(Producer-Scrounger,PS)模型的新型群体随机搜索算法。尽管该算法在解决众多问题中表现优越,但其依然面临着早熟和易陷入局部最优的问题,为此,提出了一种基于一般反向学习策略的群搜索优化算法(GOGSO)。该算法利用反向学习策略来产生反向种群,然后对当前种群和反向种群进行精英选择。通过对比实验表明,该方法效果良好。

Group search optimizer（GSO）is a new swarm intelligence algorithms based on the producer-scrounger model.GSO has been shown to yield good performance for solving various optimization problems.However,it tends to suffer from premature convergence and get stuck in local minima.This paper proposed an enhanced GSO algorithm called GOGSO,which employs generalized opposition-based learning to transform the current population into a new opposition population and uses an elite selection mechanism on the two populations.Experiments were conducted on a comprehensive set of benchmark functions.The results show that OGSO obtains promising performance.