On the Computational Study of Artificial Fish Swarm Algorithm and its Improvement

Abstract:

Artificial Fish Swarm Algorithm (AFSA) since 2002 has been proposed by Dr. Li Xiao-lei more than ten years, and has been widely used in various engineering fields. However, since a lot of comprehensive standard running tests has not yet been made with the algorithm, it has not yet been unanimously recognized by the international academic community. By 34 Benchmark Functions tested with AFSA, the result evaluation for functions that are applicable and not applicable by AFSA is summarized. Also in order to overcome the drawbacks of Global Artificial Fish Swarm Algorithm (GAFSA) such as slow convergence and low precision, a modified GAFSA(MS_GAFSA) is proposed. Combined with GAFSA and Modified Simplex, the algorithm can improve the convergence speed and precision of optimization. When GAFSA converges to the global optimum nearby, a simplex is constructed and the algorithm switches to Modified Simplex method which will continue to optimize until a certain stop condition is satisfied. Take the best point of simplex vertex at this time as the optimal value. The computational results on 34 Benchmark functions show that MS_GAFSA does improve in optimizing accuracy and convergence speed.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 1480-1484
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.1480
• Online since: November 2014
• Authors: Pei Zhen Peng, Yi Yu, Zhao Jia Wang, Min Jiang
• Keywords: Artificial Fish Swarm Algorithm, Global Optimization, Simplex Method

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