Empirical Study of Artificial Fish Swarm Algorithm

TL;DR

This paper empirically investigates the artificial fish swarm algorithm (AFSA), focusing on adaptive parameter tuning of visual and step to enhance its global and local search capabilities, validated on benchmark functions.

Contribution

It introduces adaptive modification strategies for AFSA parameters, improving its performance by balancing exploration and exploitation during execution.

Findings

Adaptive parameter strategies significantly improve AFSA performance.

Experimental results show enhanced optimization on benchmark functions.

Balancing local and global search improves convergence.

Abstract

Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence optimization algorithms that works based on population and stochastic search. In order to achieve acceptable result, there are many parameters needs to be adjusted in AFSA. Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters. In standard AFSA, these two parameters remain constant until the algorithm termination. Large values of these parameters increase the capability of algorithm in global search, while small values improve the local search ability of the algorithm. In this paper, we empirically study the performance of the AFSA and different approaches to balance between local and global exploration have been tested based on the adaptive modification of visual and step during algorithm execution. The proposed approaches have been evaluated based on the four well-known benchmark functions. Experimental results show considerable positive impact on the performance of AFSA.