《计算机应用研究》|Application Research of Computers

求解连续空间优化问题的改进蜂群算法

Modified artificial bee colony algorithm for solving continuous space optimization problems

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作者 王永琦,吴飞,孙建华
机构 1.上海工程技术大学 电子电气工程学院,上海 201620;2.湖南大学 信息科学与工程学院,长沙 410082
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文章编号 1001-3695(2018)03-0658-03
DOI 10.3969/j.issn.1001-3695.2018.03.004
摘要 为了有效地解决人工蜂群算法容易陷入局部最优的缺陷,提出了一种改进蜂群算法。利用反向学习方法构建初始种群,以提高初始化解的质量。同时,利用分布估计算法构造优秀个体解空间的概率模型来进行邻域搜索,以改善算法的搜索性能并防止陷入局部最优。对连续空间优化问题进行了仿真实验,结果表明改进算法具有较快的收敛速度,全局寻优能力显著提高。
关键词 人工蜂群算法;连续空间优化;反向学习;分布估计算法
基金项目 国家自然科学基金资助项目(F020207)
上海市科委资助项目(13510501400)
上海市工程技术大学《信号与系统》平台课程建设项目(k201602004)
本文URL http://www.arocmag.com/article/01-2018-03-004.html
英文标题 Modified artificial bee colony algorithm for solving continuous space optimization problems
作者英文名 Wang Yongqi, Wu Fei, Sun Jianhua
机构英文名 1.SchoolofElectronic&ElectricalEngineeringScience,ShanghaiUniversityofEngineering,Shanghai201620,China;2.CollegeofInformationScience&Engineering,HunanUniversity,Changsha410082,China
英文摘要 This paper proposed a modified artificial bee colony algorithm to tackle the dilemma of easily trapping in local optimum in original artificial bee colony. Firstly, it applied an opposition-based learning method for the initial population generation, which aimed to improve the quality of initial solutions. Meanwhile, it employed the estimation of distribution metaheuristic to established the probability model of solution domain about good individuals for neighbor search. This operation was capable of improving the searching performance and avoiding local optimum. Finally, it conducted the simulations to tackle the continuous space optimization problems. The experimental results demonstrate that the modified algorithm has fast constringency speed and the global optimization capability is enhanced.
英文关键词 artificial bee colony(ABC) algorithm; continuous space optimization; opposition-based learning; estimation of distribution algorithm(EDA)
参考文献 查看稿件参考文献
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收稿日期 2016/11/8
修回日期 2016/12/19
页码 658-660,704
中图分类号 TP183;TP301.6
文献标志码 A