子种群规模可变的多种群人工蜂群算法

针对人工蜂群算法开发能力不足的问题,提出一种子种群规模可变的多种群人工蜂群算法(DMABCPS).在算法中,以个体均值位置作为中心点将整个种群划分成多个子种群;雇佣蜂阶段使用三种不同策略协同搜索,保证对优良种群的开发、中间种群的平衡和较差种群的探索;观察蜂阶段采用基于成功率的选择机制对两个搜索策略进行自适应选择;此外,算法建立了新的概率选择模型,对子种群以及其内部个体进行选择.最后,通过22个标准函数测试集验证了该算法比得上一些目前较优的算法.

Multi-population artificial bee colony algorithm with variable population size

In order to solve the insufficient development ability of artificial bee colony algorithm, this paper proposed a multi-population artificial bee colony algorithm with variable population size(DMABCPS). It divided the whole population into several subpopulations with the individual mean position as the central point. It used three different strategies in the employer bee phase to ensure the exploitation of the good population, the balance of the intermediate population and the exploration of the poor population. In the onlooker bee, it selected two search strategies adaptively based on the selection mechanism of success rate. In addition, it established a new probabilistic selection model to select subpopulations and their internal individuals. Finally, it used 22 standard function test sets to verify that the proposed algorithm is superior to current algorithms.