多阶段的郊狼优化算法

doi:10.16088/j.issn.1001-6600.2022110604

摘要/Abstract

摘要： 为了解决更复杂的优化问题,本文对原始郊狼算法进行研究,提出一种多阶段的郊狼优化算法(MCOA),将算法分为前、中、后期。在前期阶段让郊狼进行正常成长,再引入旋转策略加强其勘探过程的搜索范围,避免过早陷入局部最优解。在中期阶段,提出“以优带差”的策略,再采用新型头狼更新方式对头狼进行更新,更好地平衡勘探和开采过程。在后期阶段,提出一种新型后期成长方式进行郊狼成长,并提出避免趋同因子,解决后期趋同的问题。采用CEC2014测试函数集进行仿真,与其他先进算法以及最新提出的郊狼算法比较,结果表明MCOA具有更好的搜索效率、收敛精度、优化性能以及稳定性。

关键词: 郊狼优化算法, 群智能算法, 测试函数, 多阶段, 策略

Abstract: In order to solve more complex optimization problem, the original algorithm of coyote is analyzed, and a kind of multi-stage coyotes optimization algorithm (MCOA)is proposed in this paper. The algorithm can be divided into before, while and post stages. Firstly, the coyote is allowed to grow normally in the early stage, and then the rotation strategy is introduced to enhance the search range of its exploration process to avoid falling into the local optimal solution prematurely. Secondly, in the middle stage, the strategy of “taking advantage of the advantages and taking advantage of the disadvantages” is proposed, and then the new regeneration method of the head wolf is adopted to update the head wolf, so as to better balance the exploration and exploitation process. Finally, in the late stage, a new type of late growth mode is proposed for coyote growth, and the convergence factor is avoided to solve the problem of late convergence. CEC2014 test function set is used for simulation. Compared with other advanced algorithms with the newly proposed Coyote algorithm, the results show that MCOA has better search efficiency, convergence accuracy, optimization performance and stability.

Key words: coyote optimization algorithm, swarm intelligence algorithm, test function, multi-stage, strategy

中图分类号: TP18

王钦甜, 沈艳军. 多阶段的郊狼优化算法[J]. 广西师范大学学报（自然科学版）, 2023, 41(3): 105-117.

WANG Qintian, SHEN Yanjun. Multi-stage Coyote Optimization Algorithm[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(3): 105-117.

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