Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems
The symbiotic organisms search (SOS) algorithm is a current effective meta-heuristic algorithm, which is been applied to solve various types of optimization problems. However, the SOS can easily lead to overexploration in the parasitism phase, and it is difficult to balance between exploration and e...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/1921584 |
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| _version_ | 1832547966227841024 |
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| author | Pengjun Zhao Sanyang Liu |
| author_facet | Pengjun Zhao Sanyang Liu |
| author_sort | Pengjun Zhao |
| collection | DOAJ |
| description | The symbiotic organisms search (SOS) algorithm is a current effective meta-heuristic algorithm, which is been applied to solve various types of optimization problems. However, the SOS can easily lead to overexploration in the parasitism phase, and it is difficult to balance between exploration and exploitation capabilities. In the present work, two extended versions of the SOS are proposed. Two different weight strategies (i.e., random-weight and adaptive-weight) are utilized to generate the weighted mutual vector, respectively. Meanwhile, the best organism is employed to produce the modified artificial parasite vector. The performance of the two improved algorithms is evaluated on 35 test functions. The results demonstrate that the proposed algorithms are able to provide very promising results. Furthermore, five real-world problems are solved by the two newly proposed methods. Experimental results demonstrate that the presented algorithms are more efficient than the compared algorithms. All the obtained results further indicate that the two proposed algorithms are competitive and provide better results when compared to a wide range of algorithms, including SOS and its five modified versions, as well as ten other meta-heuristic algorithms. |
| format | Article |
| id | doaj-art-1970fbd3b4824a6da95397be76e9e15f |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1970fbd3b4824a6da95397be76e9e15f2025-02-03T06:42:45ZengWileyComplexity1099-05262023-01-01202310.1155/2023/1921584Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization ProblemsPengjun Zhao0Sanyang Liu1School of Mathematics and StatisticsSchool of Mathematics and StatisticsThe symbiotic organisms search (SOS) algorithm is a current effective meta-heuristic algorithm, which is been applied to solve various types of optimization problems. However, the SOS can easily lead to overexploration in the parasitism phase, and it is difficult to balance between exploration and exploitation capabilities. In the present work, two extended versions of the SOS are proposed. Two different weight strategies (i.e., random-weight and adaptive-weight) are utilized to generate the weighted mutual vector, respectively. Meanwhile, the best organism is employed to produce the modified artificial parasite vector. The performance of the two improved algorithms is evaluated on 35 test functions. The results demonstrate that the proposed algorithms are able to provide very promising results. Furthermore, five real-world problems are solved by the two newly proposed methods. Experimental results demonstrate that the presented algorithms are more efficient than the compared algorithms. All the obtained results further indicate that the two proposed algorithms are competitive and provide better results when compared to a wide range of algorithms, including SOS and its five modified versions, as well as ten other meta-heuristic algorithms.http://dx.doi.org/10.1155/2023/1921584 |
| spellingShingle | Pengjun Zhao Sanyang Liu Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems Complexity |
| title | Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems |
| title_full | Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems |
| title_fullStr | Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems |
| title_full_unstemmed | Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems |
| title_short | Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems |
| title_sort | dynamic weighted symbiotic organisms search algorithm for global optimization problems |
| url | http://dx.doi.org/10.1155/2023/1921584 |
| work_keys_str_mv | AT pengjunzhao dynamicweightedsymbioticorganismssearchalgorithmforglobaloptimizationproblems AT sanyangliu dynamicweightedsymbioticorganismssearchalgorithmforglobaloptimizationproblems |