IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems
Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila and African vulture in nature, respectively. AO has powerful global exploration capability, whereas its local explo...
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022512?viewType=HTML |
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| author | Yaning Xiao Yanling Guo Hao Cui Yangwei Wang Jian Li Yapeng Zhang |
| author_facet | Yaning Xiao Yanling Guo Hao Cui Yangwei Wang Jian Li Yapeng Zhang |
| author_sort | Yaning Xiao |
| collection | DOAJ |
| description | Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila and African vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising exploitation capability but insufficient exploration mechanisms. Based on the characteristics of both algorithms, in this paper, we propose an improved hybrid AO and AVOA optimizer called IHAOAVOA to overcome the deficiencies in the single algorithm and provide higher-quality solutions for solving global optimization problems. First, the exploration phase of AO and the exploitation phase of AVOA are combined to retain the valuable search competence of each. Then, a new composite opposition-based learning (COBL) is designed to increase the population diversity and help the hybrid algorithm escape from the local optima. In addition, to more effectively guide the search process and balance the exploration and exploitation, the fitness-distance balance (FDB) selection strategy is introduced to modify the core position update formula. The performance of the proposed IHAOAVOA is comprehensively investigated and analyzed by comparing against the basic AO, AVOA, and six state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Experimental results demonstrate that IHAOAVOA achieves superior solution accuracy, convergence speed, and local optima avoidance than other comparison methods on most test functions. Furthermore, the practicality of IHAOAVOA is highlighted by solving five engineering design problems. Our findings reveal that the proposed technique is also highly competitive and promising when addressing real-world optimization tasks. The source code of the IHAOAVOA is publicly available at https://doi.org/10.24433/CO.2373662.v1. |
| first_indexed | 2024-04-12T06:14:50Z |
| format | Article |
| id | doaj.art-560823597933449f80550a55da553a27 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-12T06:14:50Z |
| publishDate | 2022-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-560823597933449f80550a55da553a272022-12-22T03:44:31ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-08-011911109631101710.3934/mbe.2022512IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problemsYaning Xiao0Yanling Guo1Hao Cui 2Yangwei Wang3Jian Li4Yapeng Zhang5College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaAquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila and African vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising exploitation capability but insufficient exploration mechanisms. Based on the characteristics of both algorithms, in this paper, we propose an improved hybrid AO and AVOA optimizer called IHAOAVOA to overcome the deficiencies in the single algorithm and provide higher-quality solutions for solving global optimization problems. First, the exploration phase of AO and the exploitation phase of AVOA are combined to retain the valuable search competence of each. Then, a new composite opposition-based learning (COBL) is designed to increase the population diversity and help the hybrid algorithm escape from the local optima. In addition, to more effectively guide the search process and balance the exploration and exploitation, the fitness-distance balance (FDB) selection strategy is introduced to modify the core position update formula. The performance of the proposed IHAOAVOA is comprehensively investigated and analyzed by comparing against the basic AO, AVOA, and six state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Experimental results demonstrate that IHAOAVOA achieves superior solution accuracy, convergence speed, and local optima avoidance than other comparison methods on most test functions. Furthermore, the practicality of IHAOAVOA is highlighted by solving five engineering design problems. Our findings reveal that the proposed technique is also highly competitive and promising when addressing real-world optimization tasks. The source code of the IHAOAVOA is publicly available at https://doi.org/10.24433/CO.2373662.v1.https://www.aimspress.com/article/doi/10.3934/mbe.2022512?viewType=HTMLaquila optimizerafrican vultures optimization algorithmhybrid algorithmcomposite opposition-based learningfitness-distance balanceglobal optimization |
| spellingShingle | Yaning Xiao Yanling Guo Hao Cui Yangwei Wang Jian Li Yapeng Zhang IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems Mathematical Biosciences and Engineering aquila optimizer african vultures optimization algorithm hybrid algorithm composite opposition-based learning fitness-distance balance global optimization |
| title | IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems |
| title_full | IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems |
| title_fullStr | IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems |
| title_full_unstemmed | IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems |
| title_short | IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems |
| title_sort | ihaoavoa an improved hybrid aquila optimizer and african vultures optimization algorithm for global optimization problems |
| topic | aquila optimizer african vultures optimization algorithm hybrid algorithm composite opposition-based learning fitness-distance balance global optimization |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022512?viewType=HTML |
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