引用本文:孟红云,位冰可.基于精英解和随机个体邻域信息的改进人工蜂群算法[J].控制与决策,2020,35(9):2169-2174
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基于精英解和随机个体邻域信息的改进人工蜂群算法
孟红云,位冰可
(西安电子科技大学数学与统计学院,西安710071)
摘要:
针对人工蜂群(ABC)算法开发能力差、收敛速度慢的缺点,分别提出适用于雇佣蜂和观察蜂阶段的搜索方程,其中前者用到精英解、随机选择个体及其邻域的有益信息,后者用到群体最优解的信息.所提出的搜索方程在一定程度上不仅能够加快改进算法的收敛速度,而且由于随机选择个体的引入在一定意义上可以保证算法的探索能力.对22个基准测试函数的仿真实验结果表明,所提出的算法在大多数测试函数上的性能优于对比算法.
关键词:  人工蜂群算法  精英解  邻域信息  欧氏距离
DOI:10.13195/j.kzyjc.2018.1797
分类号:TP18
基金项目:国家自然科学基金项目(61401322,61877066).
An improved artificial bee colony algorithm based on elite solution and random individual neighborhood information
MENG Hong-yun,WEI Bing-ke
(School of Mathematics and Statistics,Xidian University,Xián710071,China)
Abstract:
Aiming at the disadvantages of the artificial bee colony(ABC) algorithm, such as poor exploitation ability and slow convergence speed, the search equations for the employed bee phase and the onlooker bee phase are proposed respectively. The former exploits the beneficial information from the elite solution, randomly selected individual and its neighborhood, and the latter exploits the information from the optimal solution of the population. The proposed search equations not only accelerate the convergence speed of the improved algorithm to some extent, but also guarantee the exploration ability of the algorithm in a certain sense due to the introduction of randomly selected individuals. The simulation results of 22 benchmark functions demonstrate that the proposed algorithm is superior to the comparison algorithms on most test functions.
Key words:  artificial bee colony algorithm  elite solution  neighborhood information  Euclidean distance

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