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Computational Intelligence and Neuroscience - Volume 2015 2015, Article ID 685404, 10 pages -

Research Article

School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, China

Cognitive Science Department, Xiamen University, Xiamen 361005, China

Received 31 May 2014; Accepted 15 December 2014

Academic Editor: Carlos M. Travieso-González

Copyright © 2015 Zhehuang Huang and Yidong Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial fish swarm algorithm AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.





Autor: Zhehuang Huang and Yidong Chen

Fuente: https://www.hindawi.com/



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