A Fusion Multiobjective Empire Split Algorithm
In the last two decades, swarm intelligence optimization algorithms have been widely studied and applied to multiobjective optimization problems. In multiobjective optimization, reproduction operations and the balance of convergence and diversity are two crucial issues. Imperialist competitive algor...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8882086 |
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| _version_ | 1832563342288355328 |
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| author | Liang Liang |
| author_facet | Liang Liang |
| author_sort | Liang Liang |
| collection | DOAJ |
| description | In the last two decades, swarm intelligence optimization algorithms have been widely studied and applied to multiobjective optimization problems. In multiobjective optimization, reproduction operations and the balance of convergence and diversity are two crucial issues. Imperialist competitive algorithm (ICA) and sine cosine algorithm (SCA) are two potential algorithms for handling single-objective optimization problems, but the research of them in multiobjective optimization is scarce. In this paper, a fusion multiobjective empire split algorithm (FMOESA) is proposed. First, an initialization operation based on opposition-based learning strategy is hired to generate a good initial population. A new reproduction of offspring is introduced, which combines ICA and SCA. Besides, a novel power evaluation mechanism is proposed to identify individual performance, which takes into account both convergence and diversity of population. Experimental studies on several benchmark problems show that FMOESA is competitive compared with the state-of-the-art algorithms. Given both good performance and nice properties, the proposed algorithm could be an alternative tool when dealing with multiobjective optimization problems. |
| format | Article |
| id | doaj-art-22e1cb6297624e758631379537a56133 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-22e1cb6297624e758631379537a561332025-02-03T01:20:21ZengWileyJournal of Control Science and Engineering1687-52491687-52572020-01-01202010.1155/2020/88820868882086A Fusion Multiobjective Empire Split AlgorithmLiang Liang0School of Accounting College, Zibo Vocational Institute, Zibo 255000, Shandong, ChinaIn the last two decades, swarm intelligence optimization algorithms have been widely studied and applied to multiobjective optimization problems. In multiobjective optimization, reproduction operations and the balance of convergence and diversity are two crucial issues. Imperialist competitive algorithm (ICA) and sine cosine algorithm (SCA) are two potential algorithms for handling single-objective optimization problems, but the research of them in multiobjective optimization is scarce. In this paper, a fusion multiobjective empire split algorithm (FMOESA) is proposed. First, an initialization operation based on opposition-based learning strategy is hired to generate a good initial population. A new reproduction of offspring is introduced, which combines ICA and SCA. Besides, a novel power evaluation mechanism is proposed to identify individual performance, which takes into account both convergence and diversity of population. Experimental studies on several benchmark problems show that FMOESA is competitive compared with the state-of-the-art algorithms. Given both good performance and nice properties, the proposed algorithm could be an alternative tool when dealing with multiobjective optimization problems.http://dx.doi.org/10.1155/2020/8882086 |
| spellingShingle | Liang Liang A Fusion Multiobjective Empire Split Algorithm Journal of Control Science and Engineering |
| title | A Fusion Multiobjective Empire Split Algorithm |
| title_full | A Fusion Multiobjective Empire Split Algorithm |
| title_fullStr | A Fusion Multiobjective Empire Split Algorithm |
| title_full_unstemmed | A Fusion Multiobjective Empire Split Algorithm |
| title_short | A Fusion Multiobjective Empire Split Algorithm |
| title_sort | fusion multiobjective empire split algorithm |
| url | http://dx.doi.org/10.1155/2020/8882086 |
| work_keys_str_mv | AT liangliang afusionmultiobjectiveempiresplitalgorithm AT liangliang fusionmultiobjectiveempiresplitalgorithm |