Crocodile optimization algorithm for solving real-world optimization problems
Abstract Nature-inspired bionic algorithms have become one of the most fascinating techniques in computational intelligence research, and have shown great potential in real-world challenging problems for their simplicity and flexibility. This paper proposes a novel nature-inspired algorithm, called...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83788-4 |
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| author | Fu Yan Jin Zhang Jianqiang Yang |
| author_facet | Fu Yan Jin Zhang Jianqiang Yang |
| author_sort | Fu Yan |
| collection | DOAJ |
| description | Abstract Nature-inspired bionic algorithms have become one of the most fascinating techniques in computational intelligence research, and have shown great potential in real-world challenging problems for their simplicity and flexibility. This paper proposes a novel nature-inspired algorithm, called the crocodile optimization algorithm (COA), which mimics the hunting strategies of crocodiles. In COA, the hunting behavior of crocodiles includes premeditation and waiting hunting. The premeditation behavior is an important hunting way for crocodiles to hide themselves from their prey and to explore more potential areas, and the waiting hunting behavior is another means by which crocodiles make surprise attacks on their prey that appears in their hunting range. The performance of the proposed COA is validated by comparing it with other competitor algorithms on 29 standard test functions and 5 real-world engineering optimization problems. The experimental results show that the comprehensive performance of COA outperforms both of its similar variants and most of other state-of-the-art algorithms, in terms of solution accuracy, robustness and convergence speed. Statistical tests also validate the potential applications of the proposed algorithm. |
| format | Article |
| id | doaj-art-5c85b3dee2a641a0957c2fdd76872f7b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5c85b3dee2a641a0957c2fdd76872f7b2025-08-20T02:53:48ZengNature PortfolioScientific Reports2045-23222024-12-0114113310.1038/s41598-024-83788-4Crocodile optimization algorithm for solving real-world optimization problemsFu Yan0Jin Zhang1Jianqiang Yang2Guizhou Big Data Academy, Guizhou UniversitySchool of Mathematics and Statistics, Guizhou UniversitySchool of Mathematics and Statistics, Guizhou UniversityAbstract Nature-inspired bionic algorithms have become one of the most fascinating techniques in computational intelligence research, and have shown great potential in real-world challenging problems for their simplicity and flexibility. This paper proposes a novel nature-inspired algorithm, called the crocodile optimization algorithm (COA), which mimics the hunting strategies of crocodiles. In COA, the hunting behavior of crocodiles includes premeditation and waiting hunting. The premeditation behavior is an important hunting way for crocodiles to hide themselves from their prey and to explore more potential areas, and the waiting hunting behavior is another means by which crocodiles make surprise attacks on their prey that appears in their hunting range. The performance of the proposed COA is validated by comparing it with other competitor algorithms on 29 standard test functions and 5 real-world engineering optimization problems. The experimental results show that the comprehensive performance of COA outperforms both of its similar variants and most of other state-of-the-art algorithms, in terms of solution accuracy, robustness and convergence speed. Statistical tests also validate the potential applications of the proposed algorithm.https://doi.org/10.1038/s41598-024-83788-4MetaheuristicsCrocodile optimization algorithm (COA)Swarm intelligenceGlobal optimization |
| spellingShingle | Fu Yan Jin Zhang Jianqiang Yang Crocodile optimization algorithm for solving real-world optimization problems Scientific Reports Metaheuristics Crocodile optimization algorithm (COA) Swarm intelligence Global optimization |
| title | Crocodile optimization algorithm for solving real-world optimization problems |
| title_full | Crocodile optimization algorithm for solving real-world optimization problems |
| title_fullStr | Crocodile optimization algorithm for solving real-world optimization problems |
| title_full_unstemmed | Crocodile optimization algorithm for solving real-world optimization problems |
| title_short | Crocodile optimization algorithm for solving real-world optimization problems |
| title_sort | crocodile optimization algorithm for solving real world optimization problems |
| topic | Metaheuristics Crocodile optimization algorithm (COA) Swarm intelligence Global optimization |
| url | https://doi.org/10.1038/s41598-024-83788-4 |
| work_keys_str_mv | AT fuyan crocodileoptimizationalgorithmforsolvingrealworldoptimizationproblems AT jinzhang crocodileoptimizationalgorithmforsolvingrealworldoptimizationproblems AT jianqiangyang crocodileoptimizationalgorithmforsolvingrealworldoptimizationproblems |