Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (10): 2985-2991.DOI: 10.11772/j.issn.1001-9081.2019030454

• Advanced computing • Previous Articles     Next Articles

Best and worst coyotes strengthened coyote optimization algorithm and its application to quadratic assignment problem

ZHANG Xinming1, WANG Doudou1, CHEN Haiyan2, MAO Wentao1, DOU Zhi1, LIU Shangwang1   

  1. 1. College of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007, China;
    2. Department of Gynecological Tumor, Hubei Cancer Hospital, Wuhan Hubei 430079, China
  • Received:2019-03-19 Revised:2019-04-30 Online:2019-10-10 Published:2019-05-28
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (U1704158), the Key Research Project of Higher Education Institutions in Henan Province (19A520026).

强化最优和最差狼的郊狼优化算法及其二次指派问题应用

张新明1, 王豆豆1, 陈海燕2, 毛文涛1, 窦智1, 刘尚旺1   

  1. 1. 河南师范大学 计算机与信息工程学院, 河南 新乡 453007;
    2. 湖北省肿瘤医院 妇瘤科, 武汉 430079
  • 通讯作者: 陈海燕
  • 作者简介:张新明(1963-),男,湖北孝感人,教授,硕士,CCF会员,主要研究方向:智能优化算法、图像分割;王豆豆(1994-),女,河南许昌人,硕士研究生,主要研究方向:智能优化算法、图像分割;陈海燕(1986-),女,湖北大冶人,住院医师,硕士,主要研究方向:医学图像处理、癌症识别、医院资源配置;毛文涛(1980-),男,河南新乡人,副教授,博士,主要研究方向:机器学习、数据挖掘;窦智(1983-),男,河南新乡人,讲师,博士,主要研究方向:图像处理、遗传算法;刘尚旺(1973-),男,河南新乡人,副教授,博士,主要研究方向:图像处理、计算机视觉。
  • 基金资助:
    国家自然科学基金资助项目(U1704158);河南省高等学校重点科研项目(19A520026)。

Abstract: In view of poor performance of Coyote Optimization Algorithm (COA), a Best and Worst coyotes strengthened COA (BWCOA) was proposed. Firstly, for growth of the worst coyote in the group, a global optimal coyote guiding operation was introduced on the basis of the optimal coyote guidance to improve the social adaptability (local search ability) of the worst coyote. Then, a random perturbation operation was embedded in the growth process of the optimal coyote in the group, which means using the random perturbation between coyotes to promote the development of the coyotes and make full play of the initiative of each coyotes in the group to improve the diversity of the population and thus to enhance the global search ability, while the growing pattern of the other coyotes kept unchanged. BWCOA was applied to complex function optimization and Quadratic Assignment Problem (QAP) using hospital department layout as an example. Experimental results on CEC-2014 complex functions show that compared with COA and other state-of-the-art algorithms, BWCOA obtains 1.63 in the average ranking and 1.68 rank mean in the Friedman test, both of the results are the best. Experimental results on 6 QAP benchmark sets show that BWCOA obtains the best mean values for 5 times. These prove that BWCOA is more competitive.

Key words: intelligent optimization algorithm, Coyote Optimization Algorithm (COA), global optimal, Quadratic Assignment Problem (QAP), hospital department location

摘要: 针对郊狼优化算法(COA)优化性能不足的问题,提出一种强化最优和最差狼的COA(BWCOA)方法。首先,对于组内最差郊狼的成长,在最优郊狼引导的基础上引入全局最优郊狼引导操作,以提高最差郊狼的社会适应能力(局部搜索能力);然后,在组内最优郊狼的成长过程中嵌入一种随机扰动操作,即以郊狼之间的随机扰动促进成长,发挥组内每个郊狼的能动性,提高种群的多样性进而强化全局搜索能力;最后,组内其他郊狼的成长方式保持不变。将BWCOA运用到复杂函数优化和以医院科室布局为例的二次指派问题(QAP)中。在CEC-2014复杂函数上的实验结果表明,与COA以及其他最先进的算法相比,BWCOA获得1.63的平均均值排名和Friedman检验中1.68的秩均值,均排名第一。另外,在6组QAP上的实验结果表明,BWCOA获得了5次均值最优的结果。实验结果均表明BWCOA具有更强的竞争性。

关键词: 智能优化算法, 郊狼优化算法, 全局最优, 二次指派问题, 医院科室定位

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