An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies
Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced versi...
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| Veröffentlicht in: | Computational intelligence and neuroscience 2019, Vol.2019 (2019), p.1-24 |
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| description | Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms. |
| doi_str_mv | 10.1155/2019/1589303 |
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However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2019/1589303</identifier><identifier>PMID: 31467516</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Analysis ; Attachment ; Benchmarking ; Biogeography ; Computer Simulation ; Engineering ; Exploitation ; Exploration ; Genetic algorithms ; Global optimization ; Heuristic methods ; Intelligence ; Learning ; Learning - physiology ; Lightning ; Mathematical optimization ; Mutation ; Optimization algorithms ; Search Engine</subject><ispartof>Computational intelligence and neuroscience, 2019, Vol.2019 (2019), p.1-24</ispartof><rights>Copyright © 2019 Tongyi Zheng and Weili Luo.</rights><rights>COPYRIGHT 2019 John Wiley & Sons, Inc.</rights><rights>Copyright © 2019 Tongyi Zheng and Weili Luo. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. 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| subjects | Algorithms Analysis Attachment Benchmarking Biogeography Computer Simulation Engineering Exploitation Exploration Genetic algorithms Global optimization Heuristic methods Intelligence Learning Learning - physiology Lightning Mathematical optimization Mutation Optimization algorithms Search Engine |
| title | An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies |
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