Abstract
Artificial Fish Swarm Algorithm (AFSA) is a kind of swarm intelligence algorithms which is usually employed in optimization problems. There are many parameters to adjust in AFSA like visual and step. Through constant initializing of visual and step parameters, algorithm is only able to do local searching or global searching. In this paper, two new adaptive methods based on fuzzy systems are proposed to control the visual and step parameters during the AFSA execution in order to control the capability of global and local searching adaptively. First method uniformly adjusts the visual and step of all fish whereas in the second method, each artificial fish has its own fuzzy controller for adjusting its visual and step parameters. Evaluations of the proposed methods were performed on eight well known benchmark functions in comparison with standard AFSA and Particle Swarm Optimization (PSO). The overall results show that proposed algorithm can be effective surprisingly.
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Yazdani, D., Nadjaran Toosi, A., Meybodi, M.R. (2010). Fuzzy Adaptive Artificial Fish Swarm Algorithm. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_34
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DOI: https://doi.org/10.1007/978-3-642-17432-2_34
Publisher Name: Springer, Berlin, Heidelberg
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