Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation
Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/4182148 |
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| author | Lin Sun Suisui Chen Jiucheng Xu Yun Tian |
| author_facet | Lin Sun Suisui Chen Jiucheng Xu Yun Tian |
| author_sort | Lin Sun |
| collection | DOAJ |
| description | Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases. |
| format | Article |
| id | doaj-art-9378c84c821c4e5e8a214aa64a7e6517 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9378c84c821c4e5e8a214aa64a7e65172025-08-20T03:37:19ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/41821484182148Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local PerturbationLin Sun0Suisui Chen1Jiucheng Xu2Yun Tian3College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing 100875, ChinaMany optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.http://dx.doi.org/10.1155/2019/4182148 |
| spellingShingle | Lin Sun Suisui Chen Jiucheng Xu Yun Tian Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation Complexity |
| title | Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation |
| title_full | Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation |
| title_fullStr | Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation |
| title_full_unstemmed | Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation |
| title_short | Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation |
| title_sort | improved monarch butterfly optimization algorithm based on opposition based learning and random local perturbation |
| url | http://dx.doi.org/10.1155/2019/4182148 |
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