非洲蜂优化算法  

African bee optimization algorithm

作　　者：刘永利[1] 李仁杰 LIU Yongli;LI Renjie(School of Computer Science and Technology,He'nan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454000

出　　处：《哈尔滨工程大学学报》2020年第5期738-746,共9页Journal of Harbin Engineering University

基　　金：国家自然科学基金项目(61872126)。

摘　　要：在粒子群算法和鲸鱼群算法等进化算法中,代际之间的信息传递仅限于相邻2代个体之间,导致全局搜索参考信息单一,难以探索所有可行搜索区域并获得最优结果。本文提出一种全新的启发式算法:非洲蜂优化算法,该算法模拟了非洲蜂种群在迁徙过程中遇到威胁时的反应模式、蜂王的指令模式与记忆模式,借此实现最优化求解。为了验证非洲蜂优化算法的有效性,本文选择23种标准函数进行了对比实验,实验结果表明非洲蜂优化算法在保留较高收敛速度的同时,也保证了较好的收敛精度。In evolutionary algorithms such as particle swarm optimization and the whale optimization algorithm,intergenerational communication only exists between two neighboring generations of individuals,which leads to insufficient reference information of a global search;therefore,it is difficult to search all feasible areas to obtain the optimal result.In this paper,a new heuristic algorithm is proposed,named the African bee optimization algorithm(ABOA).This algorithm simulates the reaction pattern of African bees,the command and memory patterns of the queen bee during the migration of the African bee population,and finally realizes the optimization solution.In the experiment,23 benchmark functions were selected for comparison.The results show that the ABOA algorithm not only features a high convergence speed but also guarantees a good convergence accuracy.

关 键 词：元启发算法 进化算法 最优化 非洲蜂 迁徙策略 收敛 精度 标准函数 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]