多策略协同的改进小龙虾优化算法及其工程应用

Improved Crayfish Optimization Algorithm with Multi-strategy Integration and Its Engineer- ing Applications

摘要

本文提出了一种多策略协同的改进小龙虾优化算法（ICOA），以解决原始 COA 算法多样性不足、收玫速度慢、易陷入局部最优等问题。首先，采用 Logistic-Tent 混沌映射替代随机初始化，提升初始解质量；其次，在迭代初期引入镜像反射学习机制，利用对称性扩展解空间以加速收敛；此外，在避暑阶段融合透镜成像的自适应反向学习以增强算法跳出局部最优的能力；最后，结合遗传算法的垂直交叉操作，提高种群多样性以强化全局搜索能力。在实验部分，基于CEC2014测试函数，分别升展对比实验和消融实验验证算法性能的提升。研究证实，所提多策略协同机制有效克服了原始 COA 的缺陷，在收敛速度、精度和鲁棒性方面均有显著提升。

Abstract

This paper proposes a multi-strategy cooperatively improved crayfish optimization algorithm（ICOA）to address the issues of insufficient diversity，slow convergence，and susceptibility to local optima in the original COA.Firstly，the Logistic-Tent chaotic mapping is employed to replace random initialization，enhancing the quality of the initial solutions.Secondly，a mirror reflection learn- ing mechanism is introduced in the early iteration stage to expand the solution space through symmetry，thereby accelerating conver- gence.Additionally，during the summer avoidance phase，adaptive opposition-based learning based on lens imaging is incorporated to improve the algorithm＇s ability to escape local optima.Finally，the vertical crossover operation from the genetic algorithm is integrated to increase population diversity and strengthen global search capabilities.In the experimental section，comparative and ablation experi- ments are conducted based on the CEC2014 test functions to validate the performance improvements of the algorithm.The research confirms that the proposed multi-strategy cooperative mechanism effectively overcomes the shortcomings of the original COA，achie- ving significant enhancements in convergence speed，accuracy，and robustness.

关键词

小龙虾优化算法 / 混沌映射 / 镜像反射学习 / 自适应反向学习 / 随机节点交配的垂直交叉操作

Key words

crayfish optimization algorithm / chaos mapping / mirror reflection learning / adaptive opposition-based learning / vertical crossover operation based on random node mating

参考文献

[1] Shami T M, El Saleh A A, Alswaitti M, et al. Particle swarm opti- mization:a comprehensive survey[J]. IEEE Access, 2022, 10: 10031-10061,doi:10.1109/access.2022.3142859.

[2] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Ad- vances in Engineering Software, 2014, 69:46-61,doi:10.1016/j.advengsoft.2013.12.007.

[3] Jia H, Rao H, Wen C, et al. Crayfish optimization algorithm[J]. Ar- tificial Intelligence Review, 2023, 56(Sup.):1919-1979.

[4] Zhang Y, Liu P, Li Y. Implementation of an enhanced crayfish opti- mization algorithm[.I]. Riomimetics, 2024, 9(6):341,doi: 10.3390/biomietics9060341.

[5] Jia H, Zhou X, Zhang J, et al. Modified crayfish optimization algo- rithm for solving multiple engineering application problems[J]. Ar- tificial Intelligence Review, 2024, 57 (5):127,doi:10.1007/s10462-024-10738-x.

[6] Chauhan S, Vashishtha G, Gupta M K, et al. Parallel structure of crayfish optimization with arithmctic optimization for classifying the friction behaviour of Ti-6Al-4V alloy for complex machinery appli- cations[J]. Knowledge-Based Systems, 2024,286:11389,doi: 10.1106/j.knosys.2024.111389.

[7] Zhang C, Ding S. A stochastic configuration network based on cha- otic sparrow search algorithm[J]. Knowledge-Based Systems, 2021,220:106924,doi:10.1106/j.knosys.2021.106924.

[8] Ewees A A, Abd Elaziz M, Oliva D. A new multi-objective optimi- zation algorithm combined with opposition-based learning[J]. Ex- pert Systems with Applications, 2021,165:113844,doi:10.1106/j.eswa.2020.113844.

[9] Price K V, Storn R M, Lampinen J A. The differential evolution al- gorithm[C]// Differential Evolution: a Practical Approach to Glob- al Optimization,2005:37-134.

[10] Forrest S. Genetic algorithms[J]. ACM Computing Surveys, 1996, 28(1):77-80.

[11] Mirjalili S, Lewis A. The whale optimization algorithm[J]. Ad- vances in Engineering Software, 2016, 95:51-67,doi:10.1016/j.advengsoft.2016.01.008.

[12] Heidari A A, Mirjalili S, Faris H, et al. Harris hawks optimization: algorithm and applications[J]. Future Generation Computer Sys- tcms, 2019, 97:849-872,doi:10.1016/j.futurc.2019.02.028.

[13] Bouaouda A, Hashim F A, Sayouti Y, et al. Pied kingfisher optimi- zer:a new bio-inspired algorithm for solving numerical optimiza- tion and industrial engineering problems[J]. Neural Computing and Applications, 2024, 36(25):15455-15513.

[14] Wang W C, Tian W C, Xu D M, et al. Arctic puffin optimization :a bio-inspired metaheuristic algorithm for solving engineering design optimization[J]. Advances in Engineering Software, 2024,195: 103694,doi :10.1016/j.advengsoft.2024.103694.

[15] Ghasemi M, Golalipour K, Zare M, et al. Flood algorithm(FLA): an efficient inspired meta-heuristic for engineering optimization[J]. The Journal of Supercomputing, 2024, 80(15):22913-23017.

[16] LI X, DING Z S. Mixed strategy improved harris hawks optimiza- tion algorithm[J]. Journal of Yunnan University(Natural Sciences Edition), 2025, 47(1):60-69.

[17] Oladejo S O, Ekwe S O, Mirjalili S. The hikingoptimization algo- rithm:a novel human-based metaheuristic approach[J]. Knowl- edge Based Systems, 2024,296:111880,doi :10.1016/j.knosys.2024.111880.

[18] 李雪, 丁正生. 混合策咯改进的哈里斯鹤优化算法[J]. 云南大学学报( 自然科学版), 2025, 47(1):60-69.

基金资助

国家自然科学基金项目(62462064)

2024 年云南省教育科学规划项目(BC24019)

云南省教育厅科学研究基金项目(2024J0105)