Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning
With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, thi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/1/39 |
| _version_ | 1797344542389698560 |
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| author | Tao Tian Zhiwei Liang Yuanfei Wei Qifang Luo Yongquan Zhou |
| author_facet | Tao Tian Zhiwei Liang Yuanfei Wei Qifang Luo Yongquan Zhou |
| author_sort | Tao Tian |
| collection | DOAJ |
| description | With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA’s performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor. |
| first_indexed | 2024-03-08T11:04:14Z |
| format | Article |
| id | doaj.art-4b99d8aef08a478791e881ce4c468c85 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T11:04:14Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-4b99d8aef08a478791e881ce4c468c852024-01-26T15:16:09ZengMDPI AGBiomimetics2313-76732024-01-01913910.3390/biomimetics9010039Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path PlanningTao Tian0Zhiwei Liang1Yuanfei Wei2Qifang Luo3Yongquan Zhou4College of Economics, Guangxi Minzu University, Nanning 530006, ChinaCollege of Electronic Information, Guangxi Minzu University, Nanning 530006, ChinaFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, MalaysiaCollege of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, ChinaCollege of Economics, Guangxi Minzu University, Nanning 530006, ChinaWith the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA’s performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.https://www.mdpi.com/2313-7673/9/1/39whale optimization algorithmfirefly algorithmopposite-based learningmobile robot path planningmulti-populationhybrid metaheuristic algorithm |
| spellingShingle | Tao Tian Zhiwei Liang Yuanfei Wei Qifang Luo Yongquan Zhou Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning Biomimetics whale optimization algorithm firefly algorithm opposite-based learning mobile robot path planning multi-population hybrid metaheuristic algorithm |
| title | Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning |
| title_full | Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning |
| title_fullStr | Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning |
| title_full_unstemmed | Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning |
| title_short | Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning |
| title_sort | hybrid whale optimization with a firefly algorithm for function optimization and mobile robot path planning |
| topic | whale optimization algorithm firefly algorithm opposite-based learning mobile robot path planning multi-population hybrid metaheuristic algorithm |
| url | https://www.mdpi.com/2313-7673/9/1/39 |
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