摘要
在人工蜂群算法中,随着优化过程的进行,蜂群的多样性会急剧降低,进而导致算法陷入局部最优.针对这一问题,提出了基于邻域分割的多种群协同进化人工蜂群算法,该算法将待解问题的解空间分割成相互独立的多个领域,在每个领域上和整个解空间上分别使用不同的蜂群来优化,并且定期进行蜜源信息的交换,来提高蜂群的多样性.使用标准函数对改进算法的优化性能进行了测试,测试结果表明改进后的算法具有更好的全局寻优能力.
In artificial bee colony algorithm,the diversity of the swarm will be reduced dramatically with the optimization process,which leads to the local optimization of the algorithm. To solve this problem,a multi swarm cooperative evolutionary artificial bee colony algorithm based on neighborhood segmentation is proposed. In this algorithm,the solution space of the problem to be solved is divided into a plurality of fields which are independent of each other. Different swarm optimizations are used in each field and the whole solution space,and regularly exchanges the information of nectar are conducted to improve the diversity of the swarm. The optimization performance of the improved algorithm is tested by using the standard function. The test results show that the improved algorithm has better global searching ability.
出处
《大连交通大学学报》
CAS
2017年第3期112-115,共4页
Journal of Dalian Jiaotong University
基金
国家“863”高技术研究发展计划资助项目(2012AA041402-4)
辽宁省教育厅优秀青年学者成长计划资助项目(LJQ2013048)
关键词
人工蜂群算法
函数优化
群智能
artificial bee colony algorithm
function optimization
swarm intelligence
