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工程科学与技术:2016,48(5):124-134
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精英区域学习的转轴人工蜂群算法
(1.江西理工大学 理学院;2.武汉大学 计算机学院 软件工程国家重点实验室)
Rosenbrock Artificial Bee Colony with Elite Region Learning
(1.School of Sci.,Jiangxi Univ. of Sci. and Technol.;2.State Key Lab. of Software Eng.,Computer School,Wuhan Univ.)
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投稿时间:2015-10-27    修订日期:2016-06-02
中文摘要: 针对人工蜂群(ABC)算法在解决复杂优化问题时容易出现收敛速度慢、开采能力不足的问题,提出了一种精英区域学习的转轴人工蜂群(ERABC)算法。在ERABC算法中,通过执行区域学习方法构建精英池,并利用精英池改进其搜索策略,同时在每一代中以一定的频率对最优解执行转轴法(RM)局部搜索。在20个包含单峰、多峰和偏移函数的基准测试函数上,分析了ERABC算法中改进策略的有效性,并与多种新近的改进ABC算法和演化算法进行了比较实验。实验结果表明,提出的算法在保证精英池中个体多样性的同时加快了算法的收敛速度,RM有效地提高了算法的开采能力。
Abstract:In order to enhance the exploitation ability of the basic artificial bee colony (ABC),a rosenbrock ABC with elite region learning (ERABC) was proposed.The proposed ERABC utilized an enhanced search strategy with elite region learning to maintain the population diversity.Moreover,the rosenbrock’s rotational direction method was employed to improve the exploitation ability.The proposed ERABC was tested on 20 benchmark functions including unimodal,multimodal,and shifted functions.The effects of the improved strategy in ERABC were experimentally investigated.Furthermore,ERABC was compared with some state of the art ABC variants and several related evolutionary algorithms.The experimental results indicated that ERABC enhances the convergence speed and exploitation ability.〖
文章编号:201501137     中图分类号:    文献标志码:
基金项目:国家自然科学基金资助项目(61662029;11401267;11461032)
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熊小峰,尹雅丽,郭肇禄,吴志健.精英区域学习的转轴人工蜂群算法[J].工程科学与技术,2016,48(5):124-134.
Xiong Xiaofeng,Yin Yali,Guo Zhaolu,Wu Zhijian.Rosenbrock Artificial Bee Colony with Elite Region Learning[J].Advanced Engineering Sciences,2016,48(5):124-134.