摘要
针对经典人工蜂群算法收敛速率较慢,后期易陷入局部最优解的不足,本文将粒子群算法中＂全局最优＂的思想引入到人工蜂群算法的改进过程,从而形成了一种新的人工蜂群改进算法——粒子蜂群算法.首先,提出了趋优度的概念,用来衡量引领蜂在有限次迭代过程中向全局最优解靠近或远离的程度,趋优度值可以评价个体的＂发展潜力＂,趋优度值越低的个体,越需要增大变异的程度,以便找到质量更优的解.其次,专门设计了一种新的蜜蜂群体——粒子蜂,在引领蜂变异阶段根据趋优度的大小将引领蜂变异为侦查蜂和粒子蜂,粒子蜂的出现在很大程度上增加了种群的多样性,拓展了算法的搜索范围.然后,通过粒子蜂群算法种群序列是一个有限齐次马尔科夫链和种群进化单调性的分析,验证了本文所提算法的种群序列依概率1收敛于全局最优解集.最后,将本文所提算法应用于多个常见测试函数,并与经典蜂群算法、近年其他文献改进蜂群算法进行了仿真对比研究,仿真结果表明本文所提算法确实加大了种群的分散度、扩宽了搜索范围,从而具有更快的收敛速度和更高的寻优精度.
With an aim to address the disadvantages of the artificial bee colony algorithm of slow convergence speed and ease of falling into the local optimum in the later period of the evolution process as well as to improve the traditional artificial bee colony algorithm,the concept of the ＂global optimum＂in particle swarm optimization is introduced. Therefore,an improved artificial bee colony algorithm,called particle bee colony（ PBC）,is proposed herein. First,the concept of degree toward optimum is proposed for measuring the degree to which the leader approaches or is removed from the＂global optimum＂in a limited iteration process. The individuals＇ values of degree toward optimum denote their＂development potentials. ＂The individuals that have a low degree toward optimum require a great mutation extent to find a good solution. Second,a new colony of bees,initiated by the particle bee,is uniquely developed. In mutation period,the leader will be changed into the scout or the particle bee according to the value of the degree toward optimum. The appearance of particle bees can increase the population diversity and expand the search area to a large extent. Next,analysis reveals that the sequence of population of the PBC is a finite homogeneous Markov chain and the population evolution process is monotonous.On the basis of the above observations,it can be proved that the population sequence of the proposed algorithm converges to the global optimum solution set with probability 1. Last,the algorithm proposed in this study is applied to numerical simulations of several classical test functions. Furthermore,the proposed algorithm is compared with the traditional artificial bee colony algorithm and other improved bee colony algorithms. The simulation results show that PBC increases the population dispersion and broadens the search area,thereby allowing the proposed algorithm to achieve fast convergence rate and high optimization accuracy.
作者
王继超
李擎
崔家瑞
左文香
赵越飞
WANG Ji-chao;LI Qing;CUl Jia-rui;ZUO Wen-xiang;ZHAO Yue-fei(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Hebei University of Water Resources and Electric Engineering,Cangzhou 061001,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2018年第7期871-881,共11页
Chinese Journal of Engineering
基金
国家自然科学基金资助项目(61673098)
关键词
人工蜂群算法
趋优度
粒子蜂
马尔科夫链
种群分散度
artificial bee colony
degree toward optimum
particle bee
Markov chain
population dispersion
