信息共享模型和组外贪心策略的郊狼优化算法

doi:10.11896/jsjkx.190400039

计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 217-224.doi: 10.11896/jsjkx.190400039

信息共享模型和组外贪心策略的郊狼优化算法

张新明^1,2, 李双倩¹, 刘艳^1,2, 毛文涛^1,2, 刘尚旺¹, 刘国奇¹

1 河南师范大学计算机与信息工程学院河南新乡453007
2 智慧商务与物联网技术河南省工程实验室河南新乡453007

收稿日期:2019-04-07 出版日期:2020-05-15 发布日期:2020-05-19
通讯作者: 张新明(xinmingzhang@126.com)
基金资助:
国家自然科学基金(U1704158);河南省高等学校重点科研项目(19A520026)

Coyote Optimization Algorithm Based on Information Sharing and Static Greed Selection

ZHANG Xin-ming^1,2, LI Shuang-qian¹, LIU Yan^1,2, MAO Wen-tao^1,2, LIU Shang-wang¹, LIU Guo-qi¹

1 College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China
2 Engineering Lab of Intelligence Business and Internet of Things of Henan Province,Xinxiang,Henan 453007,China

Received:2019-04-07 Online:2020-05-15 Published:2020-05-19
About author:ZHANG Xin-ming,born in 1963,professor,master's supervisor,is a member of China Computer Federation.His main research interests include intelligent optimization algorithm and image segmentation
Supported by:
This work was supported by the National Natural Science Foundation of China (U1704158) and Key Research Projects of Higher Education Institutions of Henan Province,China (19A520026).

摘要/Abstract

摘要： 郊狼优化算法(Coyote Optimization Algorithm,COA)是最近提出的一种新颖群智能优化算法,具有较大的应用潜力,但存在运行时间长和搜索能力不足等问题。因此,文中提出了一种改进的COA,即基于信息共享和组外(静态)贪心的COA(COA based on Information sharing and Static greed selection,ISCOA)。首先,构建一种新型的信息共享模型,用于子群所有郊狼的成长,在郊狼成长前期,共享信息差异性大,以增加种群的多样性,在效狼成长后期,共享信息差异性小,以强化开采能力;其次,构建一种新的组内成长方式,即前期主要采用信息共享模型的成长方式,以郊狼的信息共享为主强化探索能力,后期主要采用原算法的成长方式,以alpha狼和文化趋势的引导为主强化开采能力;最后,将原算法的组内贪心算法改成组外贪心算法,即静态贪心算法,以便提高算法的稳定性和实现目标函数计算等的并行处理,提高运行速度。大量复杂的CEC2017函数优化实验结果表明,与COA相比,ISCOA在29个10维和30维函数上分别获得了23和24个函数的优势,其平均运行时间分别是COA的86.3%和85.7%,降低了运行时间;与7个最先进的算法相比,ISCOA在10维和30维函数上的平均排名分别是1.48和1.69,分别获得了17和18个第一,具有更好的优化效果。运用于实际工程问题的实验结果表明,ISCOA得到了最好的结果,证明了ISCOA有更强的搜索能力和竞争性以及更好的应用前景。

关键词: 郊狼优化算法, 开采能力, 群智能优化算法, 贪心算法, 探索能力

Abstract: Coyote Optimization Algorithm (COA) is a novel intelligent optimization algorithm recently proposed and has great application potential,but it has some problems such as long running time and insufficient search ability.This paper proposes an improved COA,namely COA based on Information sharing and Static greed selection (ISCOA).Firstly,a new information sharing model is constructed and applied to the growth of all coyotes in the subgroup,the difference of the sharing information is larger in the early growth so as to increase the population diversity,and the one is smaller in the late growth to be beneficial to exploitation.Secondly,a new intra-group growth mode is constructed,that is to say,a new growth way is adopted in the early stage,mainly based on the information sharing model,to strengthen the growth process to improve the exploration ability,and the growth method of the original algorithm is kept in the later stage,mainly based on the guidance of the alpha wolf and the cultural trend,to strengthen the exploiting ability.Finally,the intragroup greedy algorithm of the original algorithm is changed into a sta-tic greedy algorithm to improve the stability of the algorithm,realize the parallel calculation of the objective function,and improve the running speed.A large number of experiment results on the complex functions from CEC2017 test set show that,compared with COA,ISCOA obtains the advantage of 23 and 24 of the 29 10-dimensional and 30-dimensional functions respectively,and its average running time is 86.3% and 85.7% of COA's on the 10-dimensional and 30-dimensional functions respectively,and its running time is decreased.Compared with the 7 state-of-the-art algorithms,the average ranking of ISCOA on the 10-dimensional and 30-dimensional functions are 1.48 and 1.69,ISCOA wins 17 and 18 times ranking the first,respectively,and obtains better optimization results.Experimental results on the practical engineering problem show that ISCOA has achieved the best results.These all proved that ISCOA has stronger search ability and more competitive,and that it has better application prospects.

Key words: Coyote optimization algorithm, Exploitation, Exploration, Greedy algorithm, Swarm intelligence optimization algorithm

中图分类号:

TP181

张新明, 李双倩, 刘艳, 毛文涛, 刘尚旺, 刘国奇. 信息共享模型和组外贪心策略的郊狼优化算法[J]. 计算机科学, 2020, 47(5): 217-224. https://doi.org/10.11896/jsjkx.190400039

ZHANG Xin-ming, LI Shuang-qian, LIU Yan, MAO Wen-tao, LIU Shang-wang, LIU Guo-qi. Coyote Optimization Algorithm Based on Information Sharing and Static Greed Selection[J]. Computer Science, 2020, 47(5): 217-224. https://doi.org/10.11896/jsjkx.190400039

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信息共享模型和组外贪心策略的郊狼优化算法

Coyote Optimization Algorithm Based on Information Sharing and Static Greed Selection

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