Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning
Abstract The Gazelle Optimization Algorithm (GOA) is a recently proposed and widely recognized metaheuristic algorithm. However, it suffers from slow convergence, low precision, and a tendency to fall into local optima when addressing practical problems. To address these limitations, we propose a Mu...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98112-x |
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| author | Lu Li Haonan Zhao Lixin Lyu Fan Yang |
| author_facet | Lu Li Haonan Zhao Lixin Lyu Fan Yang |
| author_sort | Lu Li |
| collection | DOAJ |
| description | Abstract The Gazelle Optimization Algorithm (GOA) is a recently proposed and widely recognized metaheuristic algorithm. However, it suffers from slow convergence, low precision, and a tendency to fall into local optima when addressing practical problems. To address these limitations, we propose a Multi-Strategy Improved Gazelle Optimization Algorithm (MIGOA). Key enhancements include population initialization based on an optimal point set, a tangent flight search strategy, an adaptive step size factor, and novel exploration strategies. These improvements collectively enhance GOA’s exploration capability, convergence speed, and precision, effectively preventing it from becoming trapped in local optima. We evaluated MIGOA using the CEC2017 and CEC2020 benchmark test sets, comparing it with GOA and eight other algorithms. The results, validated by the Wilcoxon rank-sum test and the Friedman mean rank test, demonstrate that MIGOA achieves average rankings of 1.80, 2.03, 2.03, and 2.70 on CEC2017 (Dim = 30/50/100) and CEC2020 (Dim = 20), respectively, outperforming the standard GOA and other high-performance optimizers. Furthermore, the application of MIGOA to three-dimensional unmanned aerial vehicle (UAV) path planning problems and 2 engineering optimization design problems further validates its potential in solving constrained optimization problems. Experimental results consistently indicate that MIGOA exhibits strong scalability and practical applicability. |
| format | Article |
| id | doaj-art-5c8a59125f2141a69f6b585c9c27780c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5c8a59125f2141a69f6b585c9c27780c2025-08-20T02:19:58ZengNature PortfolioScientific Reports2045-23222025-04-0115114110.1038/s41598-025-98112-xMulti-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planningLu Li0Haonan Zhao1Lixin Lyu2Fan Yang3School of Information and Artificial Intelligence, Anhui Business CollegeSchool of Mathematics and Statistics, Northeastern University at QinhuangdaoSchool of Information and Artificial Intelligence, Anhui Business CollegeSchool of Information and Artificial Intelligence, Anhui Business CollegeAbstract The Gazelle Optimization Algorithm (GOA) is a recently proposed and widely recognized metaheuristic algorithm. However, it suffers from slow convergence, low precision, and a tendency to fall into local optima when addressing practical problems. To address these limitations, we propose a Multi-Strategy Improved Gazelle Optimization Algorithm (MIGOA). Key enhancements include population initialization based on an optimal point set, a tangent flight search strategy, an adaptive step size factor, and novel exploration strategies. These improvements collectively enhance GOA’s exploration capability, convergence speed, and precision, effectively preventing it from becoming trapped in local optima. We evaluated MIGOA using the CEC2017 and CEC2020 benchmark test sets, comparing it with GOA and eight other algorithms. The results, validated by the Wilcoxon rank-sum test and the Friedman mean rank test, demonstrate that MIGOA achieves average rankings of 1.80, 2.03, 2.03, and 2.70 on CEC2017 (Dim = 30/50/100) and CEC2020 (Dim = 20), respectively, outperforming the standard GOA and other high-performance optimizers. Furthermore, the application of MIGOA to three-dimensional unmanned aerial vehicle (UAV) path planning problems and 2 engineering optimization design problems further validates its potential in solving constrained optimization problems. Experimental results consistently indicate that MIGOA exhibits strong scalability and practical applicability.https://doi.org/10.1038/s41598-025-98112-xGazelle optimization algorithmMetaheuristic algorithmGlobal optimizationUAV path planningNumerical optimization |
| spellingShingle | Lu Li Haonan Zhao Lixin Lyu Fan Yang Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning Scientific Reports Gazelle optimization algorithm Metaheuristic algorithm Global optimization UAV path planning Numerical optimization |
| title | Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning |
| title_full | Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning |
| title_fullStr | Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning |
| title_full_unstemmed | Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning |
| title_short | Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning |
| title_sort | multi strategy improved gazelle optimization algorithm for numerical optimization and uav path planning |
| topic | Gazelle optimization algorithm Metaheuristic algorithm Global optimization UAV path planning Numerical optimization |
| url | https://doi.org/10.1038/s41598-025-98112-x |
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