摘要
为解决粒子群算法在解决多峰问题时容易陷入局部最优的问题,对粒子群算法和人工蜂群算法进行研究,提出一种新的融合算法。采用多种群粒子群方法进化,每次进化后将各子群中的最优粒子重新组合一个新的群体,利用人工蜂群模式进化得到全局最优个体;将全局最优个体反馈到粒子群各子群的进化模式中,以提高算法的收敛速度。将10个测试函数的仿真结果与一些改进的粒子群和标准人工蜂群算法进行了比较,比较结果表明,融合算法有7个测试函数的测试效果最好,其中4个为单峰函数,3个为多峰函数;该算法具有良好的全局搜索能力和较快的收敛速度。
To solve the problem that when the particle swarm optimization algorithm solved multimodal,it was easy to fall into local optimal,a new hybrid algorithm based on multi-swarm particle swarm optimization and artificial bee colony was proposed.The new algorithm used Multi-swarm particle swarm optimization,after each evolution,grouped the best particles in the subswarms into a bee group and used artificial bee colony algorithm to evolve it.After that,the best particle of the artificial bee colony algorithm were fed back to the particle swarm optimization,in order to improve the convergence speed of the algorithm.The simulation results of the problem in 10 test functions showed that,compared with other improved PSO variants or artificial bee colony algorithm,the hybrid algorithm was better than other algorithms in 7 test functions including four single peak functions and three multimodal functions.It showed that the hybrid algorithm had good global search ability and faster convergence speed.
出处
《计算机工程与设计》
CSCD
北大核心
2014年第6期2250-2254,共5页
Computer Engineering and Design
基金
国家自然科学基金项目(51205372)
河南省科技厅科技攻关基金项目(112102210445)
关键词
粒子群算法
人工蜂群算法
融合算法
群体智能算法
人工智能
particle swarm optimization
artificial bee colony
hybrid algorithm
swarm intelligence algorithm
artificial intelligence
