A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior
Abstract This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01763-y |
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| _version_ | 1823861502178754560 |
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| author | Adam Robson Kamlesh Mistry Wai-Lok Woo |
| author_facet | Adam Robson Kamlesh Mistry Wai-Lok Woo |
| author_sort | Adam Robson |
| collection | DOAJ |
| description | Abstract This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-inspired optimization algorithms and a mechanism that creates a continuing variance within population groupings, while attempting to maintain levels of computational simplicity that have helped nature-inspired optimization algorithms gain notoriety within the field of feature selection. Through this functionality, the proposed framework seeks to increase search diversity within the population swarm to address issues such as premature convergence, and oscillations within the swarm. The proposed frameworks have shown promising results when implemented into the Bat algorithm (BA), Firefly algorithm (FA), and Particle Swarm Optimization algorithm (PSO), all of which are popular when applied to the field of feature selection, and have been shown to perform well in a variety of domains, gaining notoriety due to their powerful search capabilities. |
| format | Article |
| id | doaj-art-c73df3c82496444383fe054e7d216799 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-c73df3c82496444383fe054e7d2167992025-02-09T13:01:05ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111212110.1007/s40747-024-01763-yA novel group-based framework for nature-inspired optimization algorithms with adaptive movement behaviorAdam Robson0Kamlesh Mistry1Wai-Lok Woo2School of Computer Science and Engineering, Faculty of Business and Technology, University of SunderlandDepartment of Computer and Information Sciences, Northumbria UniversityDepartment of Computer and Information Sciences, Northumbria UniversityAbstract This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-inspired optimization algorithms and a mechanism that creates a continuing variance within population groupings, while attempting to maintain levels of computational simplicity that have helped nature-inspired optimization algorithms gain notoriety within the field of feature selection. Through this functionality, the proposed framework seeks to increase search diversity within the population swarm to address issues such as premature convergence, and oscillations within the swarm. The proposed frameworks have shown promising results when implemented into the Bat algorithm (BA), Firefly algorithm (FA), and Particle Swarm Optimization algorithm (PSO), all of which are popular when applied to the field of feature selection, and have been shown to perform well in a variety of domains, gaining notoriety due to their powerful search capabilities.https://doi.org/10.1007/s40747-024-01763-yClassificationFeature selectionNature-inspired algorithmsOptimization |
| spellingShingle | Adam Robson Kamlesh Mistry Wai-Lok Woo A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior Complex & Intelligent Systems Classification Feature selection Nature-inspired algorithms Optimization |
| title | A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior |
| title_full | A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior |
| title_fullStr | A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior |
| title_full_unstemmed | A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior |
| title_short | A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior |
| title_sort | novel group based framework for nature inspired optimization algorithms with adaptive movement behavior |
| topic | Classification Feature selection Nature-inspired algorithms Optimization |
| url | https://doi.org/10.1007/s40747-024-01763-y |
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