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Log-linear model based behavior selection method for artificial fish swarm algorithm.

Huang Z, Chen Y - Comput Intell Neurosci (2015)

Bottom Line: In past several years, AFSA has been successfully applied in many research and application areas.How to construct and select behaviors of fishes are an important task.Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, China ; Cognitive Science Department, Xiamen University, Xiamen 361005, China.

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.

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Mentions: Sphere function is a single-peak function, we can find the optimal value to be 0 through the analysis for function expression, and the function image is shown in Figure 6.


Log-linear model based behavior selection method for artificial fish swarm algorithm.

Huang Z, Chen Y - Comput Intell Neurosci (2015)

The image of Sphere function.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4321098&req=5

fig6: The image of Sphere function.
Mentions: Sphere function is a single-peak function, we can find the optimal value to be 0 through the analysis for function expression, and the function image is shown in Figure 6.

Bottom Line: In past several years, AFSA has been successfully applied in many research and application areas.How to construct and select behaviors of fishes are an important task.Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, China ; Cognitive Science Department, Xiamen University, Xiamen 361005, China.

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.

Show MeSH