[1]刘晓芳,柳培忠,骆炎民,等.一种增强局部搜索能力的改进人工蜂群算法[J].智能系统学报,2017,12(05):684-693.[doi:10.11992/tis.201612026]
 LIU Xiaofang,LIU Peizhong,LUO Yanmin,et al.Improved artificial bee colony algorithm based on enhanced local search[J].CAAI Transactions on Intelligent Systems,2017,12(05):684-693.[doi:10.11992/tis.201612026]
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一种增强局部搜索能力的改进人工蜂群算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年05期
页码:
684-693
栏目:
出版日期:
2017-10-25

文章信息/Info

Title:
Improved artificial bee colony algorithm based on enhanced local search
作者:
刘晓芳1 柳培忠1 骆炎民2 范宇凌1
1. 华侨大学 工学院, 福建 泉州 362021;
2. 华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
LIU Xiaofang1 LIU Peizhong1 LUO Yanmin2 FAN Yuling1
1. Engineering school, Huaqiao University, Quanzhou 362021, China;
2. School of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
人工蜂群算法高维混沌系统适应度评价搜索策略优化算法演化算法收敛性分析精度分析智能算法
Keywords:
artificial bee colony algorithmhigh-dimension chaotic systemfitness evaluationsearch tacticsoptimization algorithmevolutionary algorithmconvergence analysisaccuracy analysisintelligent algorithm
分类号:
TP18
DOI:
10.11992/tis.201612026
摘要:
针对人工蜂群算法初始化群体分布不均匀和局部搜索能力弱的问题,本文提出了一种增强局部搜索能力的人工蜂群算法(ESABC)。首先,在种群初始化阶段采用高维洛伦兹混沌系统,得到遍历性好、有规律的初始群体,避免了随机初始化的盲目性。然后,采用基于对数函数的适应度评价方式,以增大种群个体间差异,减小选择压力,避免过早收敛。最后,在微分进化算法的启发下,提出了一种新的搜索策略,采用当前种群中的最佳个体来引导下一代的更新,以提高算法的局部搜索能力。通过对12个经典测试函数的仿真实验,并与其他经典的改进人工蜂群算法对比,结果表明:本文算法具有良好的寻优性能,无论在解的精度还是收敛速度方面效果都有所提高。
Abstract:
The shortcomings of the artificial bee colony algorithm (ABC) are its uneven initial population distribution and weak local search. In this paper, we propose an ABC algorithm based on enhanced local search (ESABC). First, we employ a high-dimension chaotic system (Lorenz system) to obtain the ergodic and regular initial populations and to avoid the blindness of random initialization in the population initialization stage. Then, we introduce improved fitness evaluation methods based on the logarithmic function to increase the differences between individuals, reduce selection pressure, and avoid premature convergence. Lastly, inspired by the differential evolution algorithm, we propose a new search tactic that uses the best individual in the contemporary population to guide the renewal of the next generation, and thereby enhance the local search ability. We examined the performance of the proposed approach with 12 classic testing functions and compared the results with the basic and other ABCs. As documented in the experimental results, the proposed algorithm exhibits good optimization performance and can improve both the accuracy and convergence speed of the algorithm.

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备注/Memo

备注/Memo:
收稿日期:2016-12-23。
基金项目:国家自然科学基金资助项目(61203242);物联网云计算平台建设资助项目(2013H2002);华侨大学研究生科研创新能力培育计划资助项目(1511322003).
作者简介:刘晓芳,女,1993年生,硕士研究生,主要研究方向为智能优化算法及其应用;柳培忠,男,1976年生,讲师,博士,美国杜克大学高级访问学者,主要研究方向为仿生智能计算、仿生图像处理技术、多维空间仿生信息学等,主持及参与课题6项,发表学术论文15篇;骆炎民,男,1975年生,副教授,博士,主要研究方向为人工智能、机器学习、图像处理、数据挖掘。主持及参与课题8项,发表学术论文16篇。
通讯作者:柳培忠.E-mail:pzliu@hqu.edu.cn
更新日期/Last Update: 2017-10-25