人工蜂群算法是最近提出的一种较有竞争力的优化技术.然而,它的搜索方程存在着探索能力强而开发能力弱的缺点.针对这一问题,受差分进化算法的启发,提出了一个改进的搜索方程.该搜索方程在最优解附近产生新的候选位置以便提高算法的开发能力.进一步,充分利用和平衡不同搜索方程的探索和开发能力,提出了一个改进的人工蜂群算法(简记为IABC).此外,为了提高算法的全局收敛速度,用反学习的初始化方法产生初始解.通过18个标准测试函数的仿真实验并与其他算法相比较,结果表明IABC算法具有良好的处理复杂数值优化问题的性能.
Abstract
Artificial bee colony (ABC) algorithm is a relatively novel optimization technique which has been shown to be competitive to other population-based algorithms.However,there is still an insufficiency in ABC regarding its solution search equation,which is good at exploration but poor at exploitation.Inspired by differential evolution (DE),we propose a modified solution search equation,which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation.Furthermore,making full use of and balancing the exploration and the exploitation of different solution search equations,we present an improved ABC (IABC for short) algorithm. In addition,to enhance the global convergence,when producing the initial population,the opposition-based learning method is employed.Experiments are conducted on a set of 18 benchmark functions.The results demonstrate good performance of the IABC algorithm in solving complex numerical optimization problems when compared with two ABC-based algorithms.
关键词
人工蜂群算法 /
差分进化算法 /
搜索方程 /
种群初始化
{{custom_keyword}} /
Key words
artificial bee colony algorithm /
differential evolution /
search equation /
population initialization
{{custom_keyword}} /
中图分类号:
TP301
{{custom_clc.code}}
({{custom_clc.text}})
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] A Bahriye,D Karaboga.A modified artificial bee colony algorithm for real-parameter optimization [J].Information Sciences,2012,192(1):120-142.
[2] 吴晓军,杨战中,赵明.均匀搜索粒子群算法[J].电子学报,2011,39(6):1261-1266. Wu Xiao-jun,Yang Zhan-zhong,Zhao Ming.A uniform searching particle swarm optimization algorithm [J].Acta Electronica Sinica,2011,39(6):1261-1266.(in Chinese)
[3] 张雪霞,陈维荣,戴朝华.带局部搜索的动态多群体自适应差分进化算法及函数优化 [J].电子学报,2010,38(8):1825-1830. Zhang Xue-xia,Chen Wei-rong,Dai Chao-hua.Dynamic multi-group self-adaptive differential evolution algorithm with local search for function optimization .Acta Electronica Sinica,2010,38(8):1825-1830.(in Chinese)
[4] F Kang,J J Li,Q Xu.Structural inverse analysis by hybrid simplex artificial bee colony algorithms [J].Computers & Structures,2009, 87(34):861-870.
[5] Q K Pan,M F Tasgetiren,P N Suganthan,T J Chua.A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem [J].Information Sciences,2011,181(12):2455-2468.
[6] A Singh.An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem [J].Applied Soft Computing,2009,9(2):625-631.
[7] N Karaboga.A new design method based on artificial bee colony algorithm for digital IIR filters [J].Journal of the Franklin Institute,2009,346(4):328-348.
[8] B Alatas.Chaotic bee colony algorithms for global numerical optimization [J].Expert Systems with Applications,2010,37(8):5682-5687.
[9] G P Zhu,K Sam.Gbest-guided artificial bee colony algorithm for numerical function optimization [J].Applied Mathematics and Computation,2010,217(7):3166-3173.
[10] S Rahnama,et al.Opposition-based differential evolution [J].IEEE Transactions on Evolutionary Computation,2008,12(1):64-79.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金 (No.60974082); 中央高校基本科研业务费专项资金 (No.K5051270002)
{{custom_fund}}