改进基于记忆的人工蜂群算法

doi:10.13190/j.jbupt.2017-037

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (5): 61-66.doi: 10.13190/j.jbupt.2017-037

改进基于记忆的人工蜂群算法

杜振鑫^1,2, 刘广钟², 韩德志²

1. 韩山师范学院计算机与信息工程学院, 广东潮州 521041;
2. 上海海事大学信息工程学院, 上海 201306

收稿日期:2017-03-27 出版日期:2017-10-28 发布日期:2017-11-21
作者简介:杜振鑫(1976-),男,讲师,E-mail:duzhenxinmail@163.com.
基金资助:
国家自然科学基金项目（61672338，61373028）

An Improved Artificial Bee Colony Algorithm with Memory

DU Zhen-xin^1,2, LIU Guang-zhong², HAN De-zhi²

1. School of Computer Information Engineering, Hanshan Normal University, Guangdong Chaozhou 521041, China;
2. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Received:2017-03-27 Online:2017-10-28 Published:2017-11-21

摘要/Abstract

摘要： 基于记忆的人工蜂群算法（ABCM）通过记住成功使用的邻居和系数指导人工蜂群下一步的搜索，需消耗多次函数评价收敛到吸引子，且始终使用与上次相同的排斥系数，造成收敛速度不快、多样性不足，易陷入局部最优解.提出一种改进ABCM （IABCM），当使用吸引系数时，候选解只消耗一次函数评价收敛到吸引子，如果候选解好于当前解，则替换当前解，否则直接删除该记忆，这样可以利用尽量小的代价得到尽量大的收益.当使用排斥系数时，该系数的数值部分重新随机生成，以增加多样性和随机性，有利于算法跳出局部最优解.在22个不同类型函数上的实验表明，IABCM在收敛速度和精度方面明显优于ABCM.

关键词: 人工蜂群算法, 记忆, 收敛速度, 函数优化

Abstract: Artificial bee colony algorithm with memory (ABCM) memorizes successful coefficients and neighbors to guide the further foraging of the artiflcial bees. ABCM consumes many function evaluations to converge to the attractors and use the same rejection coefficients as last time, which easily results in slow convergence, low population diversity and falling into the local minima. In the improved ABCM (IABCM), the candidates converge to the attractors consuming only one function evaluation, and the candidate will replace the current solution if the former is better than the latter. Otherwise, the memory will be deleted directly. By doing so, IABCM can get the most profit at a minimum cost. When the rejection coefficients are used, the numeric parts will be regenerated randomly to enhance the diversity and randomness, which is beneficial to help the algorithm to escape the local minima. Experiments on 22 functions with different characteristics demonstrate that the IABCM is significantly better than ABC and ABCM in terms of solution quality and convergence speed.

Key words: artiflcial bee colony, memory, convergence speed, function optimization

中图分类号:

TP18

杜振鑫, 刘广钟, 韩德志. 改进基于记忆的人工蜂群算法[J]. 北京邮电大学学报, 2017, 40(5): 61-66.

DU Zhen-xin, LIU Guang-zhong, HAN De-zhi. An Improved Artificial Bee Colony Algorithm with Memory[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(5): 61-66.

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改进基于记忆的人工蜂群算法

An Improved Artificial Bee Colony Algorithm with Memory

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