EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems
The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABO...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823011432 |
| _version_ | 1797341473657585664 |
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| author | Kai He Yong Zhang Yu-Kun Wang Rong-He Zhou Hong-Zhi Zhang |
| author_facet | Kai He Yong Zhang Yu-Kun Wang Rong-He Zhou Hong-Zhi Zhang |
| author_sort | Kai He |
| collection | DOAJ |
| description | The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABOA) is proposed in this paper. First, a new adaptive fragrance model is designed, which provided a finer fragrance perception way and effectively enhanced the convergence speed and accuracy. Second, Lévy flight with high-frequency short-step jumping and low-frequency long-step walking is adopted to help the algorithm jump out of the local optimum. Third, the dimension learning-based hunting is employed to enhance information exchange by creating neighbors for each butterfly, thus improving the balance between local and global search and maintaining population diversity. In addition, the Fitness-Distance-Constraint (FDC) method is introduced to enhance constraint handling in EABOA (named FDC-EABOA). The proposed EABOA is compared with 8 well-known algorithms and 8 BOA variants in CEC 2022 test suite and the results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed rank, Quade rank and multiple comparisons, analysis of variance (ANOVA) and range analysis. Finally, EABOA and FDC-EABOA are applied to seven engineering problems (parameter identification of photovoltaic module model, speed reducer design, tension/compression spring design, pressure vessel design, gear train design, welded beam design, SOPWM for 3-level inverters), and metrics such as Improvement Index (IF) and Mean Constraint Violation (MV) confirm that the proposed algorithms are satisfactory. Experimental results and statistical analysis show that the proposed algorithms outperform the comparison algorithms and demonstrate the strong potential for solving numerical optimization and engineering design problems. |
| first_indexed | 2024-03-08T10:18:49Z |
| format | Article |
| id | doaj.art-782008dac999466f82473071a4129142 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-08T10:18:49Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-782008dac999466f82473071a41291422024-01-28T04:21:03ZengElsevierAlexandria Engineering Journal1110-01682024-01-0187543573EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problemsKai He0Yong Zhang1Yu-Kun Wang2Rong-He Zhou3Hong-Zhi Zhang4School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, PR ChinaCorresponding author.; School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, PR ChinaThe butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABOA) is proposed in this paper. First, a new adaptive fragrance model is designed, which provided a finer fragrance perception way and effectively enhanced the convergence speed and accuracy. Second, Lévy flight with high-frequency short-step jumping and low-frequency long-step walking is adopted to help the algorithm jump out of the local optimum. Third, the dimension learning-based hunting is employed to enhance information exchange by creating neighbors for each butterfly, thus improving the balance between local and global search and maintaining population diversity. In addition, the Fitness-Distance-Constraint (FDC) method is introduced to enhance constraint handling in EABOA (named FDC-EABOA). The proposed EABOA is compared with 8 well-known algorithms and 8 BOA variants in CEC 2022 test suite and the results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed rank, Quade rank and multiple comparisons, analysis of variance (ANOVA) and range analysis. Finally, EABOA and FDC-EABOA are applied to seven engineering problems (parameter identification of photovoltaic module model, speed reducer design, tension/compression spring design, pressure vessel design, gear train design, welded beam design, SOPWM for 3-level inverters), and metrics such as Improvement Index (IF) and Mean Constraint Violation (MV) confirm that the proposed algorithms are satisfactory. Experimental results and statistical analysis show that the proposed algorithms outperform the comparison algorithms and demonstrate the strong potential for solving numerical optimization and engineering design problems.http://www.sciencedirect.com/science/article/pii/S1110016823011432Butterfly optimization algorithmAdaptive fragranceLévy flightDimension learning-based huntingNumerical optimizationEngineering design problems |
| spellingShingle | Kai He Yong Zhang Yu-Kun Wang Rong-He Zhou Hong-Zhi Zhang EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems Alexandria Engineering Journal Butterfly optimization algorithm Adaptive fragrance Lévy flight Dimension learning-based hunting Numerical optimization Engineering design problems |
| title | EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems |
| title_full | EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems |
| title_fullStr | EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems |
| title_full_unstemmed | EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems |
| title_short | EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems |
| title_sort | eaboa enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems |
| topic | Butterfly optimization algorithm Adaptive fragrance Lévy flight Dimension learning-based hunting Numerical optimization Engineering design problems |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823011432 |
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