[1]刘晓芳,柳培忠,骆炎民,等.平衡搜索的改进人工蜂群算法[J].华侨大学学报(自然科学版),2019,40(1):128-132.[doi:10.11830/ISSN.1000-5013.201612038]
　LIU Xiaofang,LIU Peizhong,LUO Yanmin,et al.Improved Artificial Bee Colony Algorithm Based on Balanced Search[J].Journal of Huaqiao University(Natural Science),2019,40(1):128-132.[doi:10.11830/ISSN.1000-5013.201612038]
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平衡搜索的改进人工蜂群算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第40卷
期数:
2019年第1期
页码:
128-132
栏目:
出版日期:
2019-01-20

文章信息/Info

Title:
Improved Artificial Bee Colony Algorithm Based on Balanced Search
文章编号:
1000-5013(2019)01-0128-05
作者:
刘晓芳1; 2;  柳培忠1; 2;  骆炎民3;  范宇凌1
1. 华侨大学 工学院, 福建 泉州 362021;2. 华侨大学 工业智能化技术与系统福建省高校工程研究中心, 福建 泉州 362021;3. 华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
LIU Xiaofang1; 2;  LIU Peizhong1; 2;  LUO Yanmin3;  FAN Yuling1
1. College of Engineering, Huaqiao University, Quanzhou 362021, China; 2. Universities Engineering Research Center of Fujian Province Industrial Intelligent Technology and Systems, Huaqiao University, Quanzhou 362021, China; 3. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
人工蜂群算法;  局部搜索;  群智能算法;  适应度评价;  搜索策略
Keywords:
artificial bee colony algorithm;  local search;  swarm intelligence algorithm;  fitness evaluation;  search strategy
分类号:
TP18
DOI:
10.11830/ISSN.1000-5013.201612038
文献标志码:
A
摘要:
针对人工蜂群(ABC)算法局部搜索能力弱的问题,提出一种平衡搜索的人工蜂群算法(BSABC).首先,采用一种基于对数函数的的适应度评价方式,用于减小选择压力,在一定程度上避免陷入局部最优.其次,受微分进化算法的启发,提出一种新的搜索策略,通过当前最优个体指导进化方向,使候选解的产生倾向于当前最优解,同时避免陷入局部最优.对6个经典测试函数进行仿真实验,并与经典的改进人工蜂群算法对比测试,结果表明:所提出的算法在收敛速度和收敛精度上都有显著的提升.
Abstract:
Aim at the drawback of artificial bee colony(ABC)algorithm with weak local search capability, an artificial bee colony algorithm based on balanced search(BSABC)is proposed. Firstly, improved fitness evaluation methods based on the logarithmic function is introduced to minimize selection pressure and avoid to fall into local optimum to a certain extent. Secondly, enlightened by the differential evolution algorithm, a novel search strategy is proposed, which conducts the evolution direction of the candidate solution, tending to the current optimal solution, and at the same time avoiding to fall into the local optimum. The simulating experiments were conducted on a benchmark suite of 6 test functions, the results demonstrate that BSABC has significant enhancement in convergent speed and convergent accuracy compared with the basic ABC algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2016-12-20
通信作者: 柳培忠(1976-),男,讲师,博士,主要从事仿生智能计算、多维空间仿生信息学的研究.E-mail:pzliu@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61203242); 福建省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)
更新日期/Last Update: 2019-01-20