AFSA-SLnO variants for enhanced global optimization
Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to a...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/39634/1/AFSA-SLnO%20Variants%20for%20Enhanced%20Global%20Optimization.pdf http://umpir.ump.edu.my/id/eprint/39634/2/AFSA-SLnO%20variants%20for%20enhanced%20global%20optimization_ABS.pdf http://umpir.ump.edu.my/id/eprint/39634/ https://doi.org/10.1007/978-981-16-8129-5_79 |
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| Summary: | Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to achieve a satisfactory global optimum. This paper presents incorporation of circle updating position from Sea Lion Optimization (SLnO) into AFSA to enhance the robustness and optimum value. Fifteen benchmarks function have been used to evaluate the performance of the proposed variants in comparison to the standard AFSA and SLnO. The proposed variants show better result compared to the standard AFSA and SLnO. |
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