Swarm Exploration Mechanism-Based Distributed Water Wave Optimization
Abstract Using sparrow search hunting mechanism to improve water wave algorithm (WWOSSA), which combines the water wave optimization (WWO) algorithm and the sparrow search algorithm (SSA), has good optimization ability and fast convergence speed. However, it still suffers from insufficient explorati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-05-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00248-z |
| _version_ | 1797827360164151296 |
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| author | Haotian Li Haichuan Yang Baohang Zhang Han Zhang Shangce Gao |
| author_facet | Haotian Li Haichuan Yang Baohang Zhang Han Zhang Shangce Gao |
| author_sort | Haotian Li |
| collection | DOAJ |
| description | Abstract Using sparrow search hunting mechanism to improve water wave algorithm (WWOSSA), which combines the water wave optimization (WWO) algorithm and the sparrow search algorithm (SSA), has good optimization ability and fast convergence speed. However, it still suffers from insufficient exploration ability and is easy to fall into local optimum. In this study, we propose a new algorithm for distributed population structure, called swarm exploration mechanism-based distributed water wave optimization (DWSA). In DWSA, an information exchange component and an optimal individual evolution component are designed to improve information exchange between individuals. This multi-part information interaction and distributed population structure algorithm can help the population algorithm to establish a balance between exploitation and exploration more effectively. We contrast DWSA with the original algorithms WWOSSA and other meta-heuristics in order to show the effectiveness of DWSA. The test set consists of 22 actual optimization issues from the CEC2011 set and 29 benchmark functions from the CEC2017 benchmark functions. In addition, an experimental comparison of the parameter values introduced in DWSA is included. According to experimental results, the proposed DWSA performs substantially better than its competitors. Assessments of the population diversity and landscape search trajectory also confirmed DWSA’s outstanding convergence. |
| first_indexed | 2024-04-09T12:47:02Z |
| format | Article |
| id | doaj.art-2d7549750a014b83ad731770b0a6450c |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-09T12:47:02Z |
| publishDate | 2023-05-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-2d7549750a014b83ad731770b0a6450c2023-05-14T11:27:12ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-05-0116112610.1007/s44196-023-00248-zSwarm Exploration Mechanism-Based Distributed Water Wave OptimizationHaotian Li0Haichuan Yang1Baohang Zhang2Han Zhang3Shangce Gao4Faculty of Engineering, University of ToyamaFaculty of Engineering, University of ToyamaFaculty of Engineering, University of ToyamaFaculty of Engineering, University of ToyamaFaculty of Engineering, University of ToyamaAbstract Using sparrow search hunting mechanism to improve water wave algorithm (WWOSSA), which combines the water wave optimization (WWO) algorithm and the sparrow search algorithm (SSA), has good optimization ability and fast convergence speed. However, it still suffers from insufficient exploration ability and is easy to fall into local optimum. In this study, we propose a new algorithm for distributed population structure, called swarm exploration mechanism-based distributed water wave optimization (DWSA). In DWSA, an information exchange component and an optimal individual evolution component are designed to improve information exchange between individuals. This multi-part information interaction and distributed population structure algorithm can help the population algorithm to establish a balance between exploitation and exploration more effectively. We contrast DWSA with the original algorithms WWOSSA and other meta-heuristics in order to show the effectiveness of DWSA. The test set consists of 22 actual optimization issues from the CEC2011 set and 29 benchmark functions from the CEC2017 benchmark functions. In addition, an experimental comparison of the parameter values introduced in DWSA is included. According to experimental results, the proposed DWSA performs substantially better than its competitors. Assessments of the population diversity and landscape search trajectory also confirmed DWSA’s outstanding convergence.https://doi.org/10.1007/s44196-023-00248-zMeta-heuristic algorithmsPopulation structureHierarchical interactionDistributed frameworkExploitation and exploration |
| spellingShingle | Haotian Li Haichuan Yang Baohang Zhang Han Zhang Shangce Gao Swarm Exploration Mechanism-Based Distributed Water Wave Optimization International Journal of Computational Intelligence Systems Meta-heuristic algorithms Population structure Hierarchical interaction Distributed framework Exploitation and exploration |
| title | Swarm Exploration Mechanism-Based Distributed Water Wave Optimization |
| title_full | Swarm Exploration Mechanism-Based Distributed Water Wave Optimization |
| title_fullStr | Swarm Exploration Mechanism-Based Distributed Water Wave Optimization |
| title_full_unstemmed | Swarm Exploration Mechanism-Based Distributed Water Wave Optimization |
| title_short | Swarm Exploration Mechanism-Based Distributed Water Wave Optimization |
| title_sort | swarm exploration mechanism based distributed water wave optimization |
| topic | Meta-heuristic algorithms Population structure Hierarchical interaction Distributed framework Exploitation and exploration |
| url | https://doi.org/10.1007/s44196-023-00248-z |
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