Improved African Vulture Optimization Algorithm Based on Quasi-oppositional Differential Evolution Operator
In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad de...
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| creator | Liu, Renju Wang, Tianlei Zhou, Jing Hao, Xiaoxi Xu, Ying Qiu, Jiongzhi |
| description | In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA while exhibiting superior performance compared to those of other swarm intelligence algorithms. |
| doi_str_mv | 10.1109/ACCESS.2022.3203813 |
| format | Article |
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The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. 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A numerical simulation experiment based on 36 different types of benchmark functions showed that the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA while exhibiting superior performance compared to those of other swarm intelligence algorithms.</description><subject>African Vulture Optimization Algorithm</subject><subject>Algorithms</subject><subject>Basic converters</subject><subject>Benchmark Function</subject><subject>Benchmarks</subject><subject>Birds</subject><subject>Convergence</subject><subject>Differential Evolution</subject><subject>Evolutionary computation</subject><subject>Exploration</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Metaheuristics</subject><subject>Operators (mathematics)</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Particle swarm optimization</subject><subject>Population</subject><subject>Quasi-oppositional Learning</subject><subject>Sociology</subject><subject>Statistics</subject><subject>Swarm intelligence</subject><subject>Swarm Intelligence Algorithm</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUctu2zAQFIoUqJHkC3IR0LMcPiXyqDpOayCAESTtldhIy5SubKokFSD9-tKREWQvSw5mZrmcoriiZEkp0dftarV-eFgywtiSM8IV5Z-KBaO1rrjk9dmH85fiMsYdyaUyJJtF8WezH4N_wb5sbXAdHMpf05CmgOV2TG7v_kFy_lC2w7MPLv3el98gZnKG7ieIrvLj6KM7cmAob5y1GPCQXL6sX_wwvYm3IwZIPlwUny0MES9P_bz4ebt-XP2o7rbfN6v2ruoEUaniFhvSd6BR6vxQIeta8T6XlbYXSjad7bFT2hLR01oq-SSsllqBkAiWAT8vNrNv72FnxuD2EF6NB2feAB-eDYTkugEN6YERiiJbUCE6rjStGSjCqK2fJOrs9XX2yr_0d8KYzM5PIS8bDWuo0FIoojKLz6wu-BgD2veplJhjSGYOyRxDMqeQsupqVjlEfFdo1YicDv8PBXaPJw</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Liu, Renju</creator><creator>Wang, Tianlei</creator><creator>Zhou, Jing</creator><creator>Hao, Xiaoxi</creator><creator>Xu, Ying</creator><creator>Qiu, Jiongzhi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | African Vulture Optimization Algorithm Algorithms Basic converters Benchmark Function Benchmarks Birds Convergence Differential Evolution Evolutionary computation Exploration Machine learning Mathematical models Metaheuristics Operators (mathematics) Optimization Optimization algorithms Particle swarm optimization Population Quasi-oppositional Learning Sociology Statistics Swarm intelligence Swarm Intelligence Algorithm |
| title | Improved African Vulture Optimization Algorithm Based on Quasi-oppositional Differential Evolution Operator |
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