A study towards improving artificial fish swarm algorithm
| dc.contributor.author | Nur Afiqah Bte Junizan | |
| dc.date.accessioned | 2021-02-19T03:51:25Z | |
| dc.date.available | 2021-02-19T03:51:25Z | |
| dc.date.issued | 2019-06 | |
| dc.description.abstract | Intelligent system method has been rapidly utilized in many fields to solve many problems. One of an intelligent system that is popular nowadays is swarm intelligent. This work focuses on the Artificial Fish Swarm Algorithms (AFSAs) which is a type of swarm intelligent algorithm. AFSA algorithms have shown good results in various applications. It is able to convergence fast and robust to reach the optimum because it can quickly search the feasible solution within a range of search spaces. However, there has been some problems with the standard AFSA algorithm that its performance sometimes deteriorates as the complexity of optimization problems increases. In such cases, this algorithm normally shows slow convergence rates, lack of ability to reach the global optimum. This is usually caused by poor ability to keep the balance between exploration and exploitation process. The aim of this research is to improve the standard AFSA algorithm through memetic AFSA algorithms. This is achieved through hybridization of the standard AFSA with a local search technique. This work proposes six memetic AFSA algorithms with the aim of improving the convergence rate and global optimum achievement. These proposed memetic AFSA algorithms have been generated through hybridizing of the standard AFSA with Evolutionary Gradient Search (EGS) technique and modified EGS technique. The performances of all proposed memetic AFSA algorithms have been evaluated on ten benchmark functions. Their convergence rates and global minimum achievements have been compared with the standard AFSA algorithm and amongst themselves in order to validate their performance. The obtained results have shown excellent global optimum achievement of the proposed algorithms particularly by AFSA-EGS-F, AFSA-EGS-FS, AFSA-mEGS-F and AFSA-mEGS-FS algorithms in comparison to the standard AFSA. Also, the AFSA-EGS-F, AFSA-EGS-FS and AFSA-mEGS-FS have shown to be good at the convergence rate compared to the standard AFSA. In conclusion, the results show that the proposed memetic AFSA algorithms have been able to converge and avoid being trapped in local minimum better than the standard AFSA. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/11387 | |
| dc.language.iso | en | en_US |
| dc.title | A study towards improving artificial fish swarm algorithm | en_US |
| dc.type | Other | en_US |
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