MCOA: A Multistrategy Collaborative Enhanced Crayfish Optimization Algorithm for Engineering Design and UAV Path Planning
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| Titel: | MCOA: A Multistrategy Collaborative Enhanced Crayfish Optimization Algorithm for Engineering Design and UAV Path Planning |
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| Autoren: | Xiao, Yaning, Cui, Hao, Khurma, Ruba Abu, Hussien, Abdelazim, Castillo, Pedro A. |
| Quelle: | International Journal of Intelligent Systems. 2025(1) |
| Schlagwörter: | crayfish optimization algorithm, engineering optimization, expanded exploration strategy, L & eacute, vy flight, specular reflection learning, vertical crossover operator |
| Beschreibung: | The crayfish optimization algorithm (COA) is a recent bionic optimization technique that mimics the summer sheltering, foraging, and competitive behaviors of crayfish. Although COA has outperformed some classical metaheuristic (MH) algorithms in preliminary studies, it still manifests the shortcomings of falling into local optimal stagnation, slow convergence speed, and exploration-exploitation imbalance in addressing intractable optimization problems. To alleviate these limitations, this study introduces a novel modified crayfish optimization algorithm with multiple search strategies, abbreviated as MCOA. First, specular reflection learning is implemented in the initial iterations to enrich population diversity and broaden the search scope. Then, the location update equation in the exploration procedure of COA is supplanted by the expanded exploration strategy adopted from Aquila optimizer (AO), endowing the proposed algorithm with a more efficient exploration power. Subsequently, the motion characteristics inherent to L & eacute;vy flight are embedded into local exploitation to aid the search agent in converging more efficiently toward the global optimum. Finally, a vertical crossover operator is meticulously designed to prevent trapping in local optima and to balance exploration and exploitation more robustly. The proposed MCOA is compared against twelve advanced optimization algorithms and nine similar improved variants on the IEEE CEC2005, CEC2019, and CEC2022 test sets. The experimental results demonstrate the reliable optimization capability of MCOA, which separately achieves the minimum Friedman average ranking values of 1.1304, 1.7000, and 1.3333 on the three test benchmarks. In most test cases, MCOA can outperform other comparison methods regarding solution accuracy, convergence speed, and stability. The practicality of MCOA has been further corroborated through its application to seven engineering design issues and unmanned aerial vehicle (UAV) path planning tasks in complex three-dimensional environments. Our findings underscore the competitive edge and potential of MCOA for real-world engineering applications. The source code for MCOA can be accessed at . |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-214937 https://doi.org/10.1155/int/5054424 |
| Datenbank: | SwePub |
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Nájsť tento článok vo Web of Science | Abstract: | The crayfish optimization algorithm (COA) is a recent bionic optimization technique that mimics the summer sheltering, foraging, and competitive behaviors of crayfish. Although COA has outperformed some classical metaheuristic (MH) algorithms in preliminary studies, it still manifests the shortcomings of falling into local optimal stagnation, slow convergence speed, and exploration-exploitation imbalance in addressing intractable optimization problems. To alleviate these limitations, this study introduces a novel modified crayfish optimization algorithm with multiple search strategies, abbreviated as MCOA. First, specular reflection learning is implemented in the initial iterations to enrich population diversity and broaden the search scope. Then, the location update equation in the exploration procedure of COA is supplanted by the expanded exploration strategy adopted from Aquila optimizer (AO), endowing the proposed algorithm with a more efficient exploration power. Subsequently, the motion characteristics inherent to L & eacute;vy flight are embedded into local exploitation to aid the search agent in converging more efficiently toward the global optimum. Finally, a vertical crossover operator is meticulously designed to prevent trapping in local optima and to balance exploration and exploitation more robustly. The proposed MCOA is compared against twelve advanced optimization algorithms and nine similar improved variants on the IEEE CEC2005, CEC2019, and CEC2022 test sets. The experimental results demonstrate the reliable optimization capability of MCOA, which separately achieves the minimum Friedman average ranking values of 1.1304, 1.7000, and 1.3333 on the three test benchmarks. In most test cases, MCOA can outperform other comparison methods regarding solution accuracy, convergence speed, and stability. The practicality of MCOA has been further corroborated through its application to seven engineering design issues and unmanned aerial vehicle (UAV) path planning tasks in complex three-dimensional environments. Our findings underscore the competitive edge and potential of MCOA for real-world engineering applications. The source code for MCOA can be accessed at . |
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| ISSN: | 08848173 1098111X |
| DOI: | 10.1155/int/5054424 |