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Abstract: Optimization problems are crucial for a wide range of engineering applications, as efficient solutions lead to better performance. This study introduces an improved coati optimization algorithm (ICOA) that overcomes the primary limitations of the original coati optimization algorithm (COA), notably its insufficient population diversity and propensity to become trapped in local optima. To address these issues, the ICOA integrates three innovative strategies: latin hypercube sampling (LHS), lé;vy-flight, and an adaptive local search. LHS is employed to ensure a diverse initial population, thereby laying a foundation for the optimization. lé;vy-flight is utilized to facilitate an efficient global search, enhancing the algorithm’s ability to explore the solution space. The adaptive local search is designed to refine solutions, enabling more precise local exploration. Together, these strategies significantly improve the population’s quality and diversity, thereby improving the algorithm’s convergence accuracy and optimization capabilities. The performance of the ICOA is tested against several established algorithms, using 12 benchmark functions. Additionally, the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem, specifically the design optimization of tension/compression springs. Simulation results show that the ICOA consistently outperforms the other algorithms, providing robust solutions for a wide range of optimization problems.

通过多策略集成改进的浣熊优化算法：从理论设计到工程应用

[1]AgushakaJO, EzugwuAE, AbualigahL, 2023. Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Computing and Applications, 35(5):4099-4131.

[2]AliES, Abd ElazimSM, AbdelazizAY, 2017. Ant lion optimization algorithm for optimal location and sizing of renewable distributed generations. Renewable Energy, 101:1311-1324.

[3]AltayEV, AltayO, ÖzçevikY, 2024. A comparative study of metaheuristic optimization algorithms for solving real-world engineering design problems. Computer Modeling in Engineering & Sciences, 139(1):1039-1094.

[4]AroraJS, 2004. Introduction to Optimum Design. 2nd Edition. Elsevier, Amsterdam, the Netherlands.

[5]BingulZ, KarahanO, 2018. A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. Journal of the Franklin Institute, 355(13):5534-5559.

[6]BraikMS, 2021. Chameleon swarm algorithm: a bio-inspired optimizer for solving engineering design problems. Expert Systems with Applications, 174:114685.

[7]BrezočnikL, Fister JrI, PodgorelecV, 2018. Swarm intelligence algorithms for feature selection: a review. Applied Sciences, 8(9):1521.

[8]CuiZH, ZhangZX, HuZM, et al., 2022. A many-objective optimization based intelligent high performance data processing model for cyber-physical-social systems. IEEE Transactions on Network Science and Engineering, 9(6):3825-3834.

[9]CuiZL, LiCQ, HuangJR, et al., 2020. An improved moth flame optimization algorithm for minimizing specific fuel consumption of variable cycle engine. IEEE Access, 8:142725-142735.

[10]DehghaniM, MontazeriZ, TrojovskáE, et al., 2023. Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259:110011.

[11]EmamMM, HousseinEH, SameeNA, et al., 2024. Breast cancer diagnosis using optimized deep convolutional neural network based on transfer learning technique and improved coati optimization algorithm. Expert Systems with Applications, 255:124581.

[12]EmaryE, ZawbaaHM, SharawiM, 2019. Impact of Lèvy flight on modern meta-heuristic optimizers. Applied Soft Computing, 75:775-789.

[13]FarisH, AljarahI, Al-BetarMA, et al., 2018. Grey wolf optimizer: a review of recent variants and applications. Neural Computing and Applications, 30(2):413-435.

[14]HashimFA, HussainK, HousseinEH, et al., 2021. Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3):1531-1551.

[15]HashimFA, HousseinEH, MostafaRR, et al., 2023. An efficient adaptive-mutated coati optimization algorithm for feature selection and global optimization. Alexandria Engineering Journal, 85:29-48.

[16]HeQ, WangL, 2007. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20(1):89-99.

[17]HeQQ, LiuH, DingGY, et al., 2023. A modified Lévy flight distribution for solving high-dimensional numerical optimization problems. Mathematics and Computers in Simulation, 204:376-400.

[18]HeiseSA, MorseHS, 2000. The DARPA JFACC program: modeling and control of military operations. Proceedings of the 39th IEEE Conference on Decision and Control, p.2551-2555.

[19]HousseinEH, SaadMR, HashimFA, et al., 2020. Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94:103731.

[20]HousseinEH, MahdyMA, BlondinMJ, et al., 2021. Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems. Expert Systems with Applications, 174:114689.

[21]HussienAG, HassanienAE, HousseinEH, et al., 2020. New binary whale optimization algorithm for discrete optimization problems. Engineering Optimization, 52(6):945-959.

[22]IaccaG, dos Santos JuniorVC, de MeloVV, 2021. An improved Jaya optimization algorithm with Lévy flight. Expert Systems with Applications, 165:113902.

[23]KarimiJ, RajabiMR, SadatiSH, et al., 2024. Multidisciplinary design optimization of a dual-spin guided vehicle. Defence Technology, 37:133-148.

[24]KennedyJ, EberhartR, 1995. Particle swarm optimization. Proceedings of International Conference on Neural Networks, p.1942-1948.

[25]LeTHH, DinhPH, VuVH, et al., 2024. A new approach to medical image fusion based on the improved extended difference-of-Gaussians combined with the coati optimization algorithm. Biomedical Signal Processing and Control, 93:106175.

[26]LiBW, YeSX, QiLiang, et al., 2024. Data correction method for low-cost gas sensors based on COA-GRU algorithm. Instrument Technique and Sensor, (3):120-126 (in Chinese).

[27]LiG, YaoY, ShenLJ, et al., 2023. Influence of yaw damper layouts on locomotive lateral dynamics performance: Pareto optimization and parameter analysis. Journal of Zhejiang University-SCIENCE A, 24(5):450-464.

[28]LiuSX, HuangFP, YanBB, et al., 2021. Optimal design of multimissile formation based on an adaptive SA-PSO algorithm. Aerospace, 9(1):21.

[29]LiuSX, LiuW, HuangF, et al., 2022. Multitarget allocation strategy based on adaptive SA-PSO algorithm. The Aeronautical Journal, 126(1300):1069-1081.

[30]LiuSX, LinZH, HuangW, et al., 2025. Current development and future prospects of multi-target assignment problem: a bibliometric analysis review. Defence Technology, 43:44-59.

[31]LiuXY, LiGQ, YangHY, et al., 2023. Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm. Expert Systems with Applications, 233:120946.

[32]MaWH, FangYW, FuWX, et al., 2023. Cooperative localisation of UAV swarm based on adaptive SA-PSO algorithm. The Aeronautical Journal, 127(1307):57-75.

[33]Martí SempereC, 2023. The problem of allocating resources to defense. Defence Studies, 23(1):86-104.

[34]MirjaliliS, MirjaliliSM, LewisA, 2014. Grey wolf optimizer. Advances in Engineering Software, 69:46-61.

[35]MirjaliliS, 2015a. The ant lion optimizer. Advances in Engineering Software, 83:80-98.

[36]MirjaliliS, 2015b. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89:228-249.

[37]Pestana BarrosC, 2004. Measuring performance in defense-sector companies in a small NATO member country. Journal of Economic Studies, 31(2):112-128.

[38]PolitisSS, ZhangZM, HanZ, et al., 2021. Stochastic analysis of network-level bridge maintenance needs using Latin hypercube sampling. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(1):04020049.

[39]RajabiMM, Ataie-AshtianiB, JanssenH, 2015. Efficiency enhancement of optimized Latin hypercube sampling strategies: application to Monte Carlo uncertainty analysis and meta-modeling. Advances in Water Resources, 76:127-139.

[40]SaeedRA, OmriM, Abdel-KhalekS, et al., 2022. Optimal path planning for drones based on swarm intelligence algorithm. Neural Computing and Applications, 34(12):10133-10155.

[41]SaheedYK, BalogunBF, OdunayoBJ, et al., 2023. Microarray gene expression data classification via Wilcoxon sign rank sum and novel grey wolf optimized ensemble learning models. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(6):3575-3587.

[42]SahooSK, SahaAK, 2022. A hybrid moth flame optimization algorithm for global optimization. Journal of Bionic Engineering, 19(5):1522-1543.

[43]ShamamiN, MehdizadehE, YazdaniM, et al., 2024. War game problem considering the mobility of weapons and targets. Journal of Engineering Research, 12(1):214-225.

[44]SharmaA, ShovalS, SharmaA, et al., 2022. Path planning for multiple targets interception by the swarm of UAVs based on swarm intelligence algorithms: a review. IETE Technical Review, 39(3):675-697.

[45]ShieldsMD, ZhangJX, 2016. The generalization of Latin hypercube sampling. Reliability Engineering & System Safety, 148:96-108.

[46]SongYY, WangFL, ChenXX, 2019. An improved genetic algorithm for numerical function optimization. Applied Intelligence, 49(5):1880-1902.

[47]SunWC, LinSD, ZhangH, et al., 2024. A reduced combustion mechanism of ammonia/diesel optimized with multi-objective genetic algorithm. Defence Technology, 34:187-200.

[48]TerzievV, NichevN, 2017. Streamlining management solutions for economic, effective and efficient spending of resources for security and defense. IJASOS-International E-Journal of Advances in Social Sciences, 3(8):640-644.

[49]WadeBM, 2019. A multi-objective optimization of ballistic and cruise missile fire plans based on damage calculations from missile impacts on an airfield defended by an air defense artillery network. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 16(2):103-117.

[50]WangCG, YanJH, LiWL, et al., 2024. Disturbances rejection optimization based on improved two-degree-of-freedom LADRC for permanent magnet synchronous motor systems. Defence Technology, 33:518-531.

[51]WangDS, TanDP, LiuL, 2018. Particle swarm optimization algorithm: an overview. Soft Computing, 22(2):387-408.

[52]WangY, GaoHL, BaoXY, et al., 2024. Load frequency control of power system based on coati optimization algorithm. Automation & Instrumentation, 39(9):37-40 (in Chinese).

[53]YangZP, YangSN, ZhouQS, et al., 2022. A joint optimization algorithm for focused energy delivery in precision electronic warfare. Defence Technology, 18(4):709-721.

[54]YaoP, WangHL, 2017. Dynamic adaptive ant lion optimizer applied to route planning for unmanned aerial vehicle. Soft Computing, 21(18):5475-5488.

[55]YaoX, LiuY, LinGM, 1999. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2):82-102.

[56]YeXW, ZhangXL, ChenYB, et al., 2024. Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm. Journal of Zhejiang University-SCIENCE A, 25(1):1-17.

[57]ZhangF, ChengL, WuMY, et al., 2020. Performance analysis of two-stage thermoelectric generator model based on Latin hypercube sampling. Energy Conversion and Management, 221:113159.

[58]ZhaoTH, WuLJ, CuiZH, et al., 2025. An adaptive strategy based multi-population multi-objective optimization algorithm. Information Sciences, 686:120913.

[59]ZhaoZQ, LiT, ShengDL, et al., 2024. Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency. Defence Technology, 39:23-41.