Artificial Fish School Algorithm for Function Optimization

Abstract

Function optimization is always being one of the important problems of scientific field. Over the past a few decades, many artificial intelligent optimizing algorithms have been invented, such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and so on. Artificial fish school algorithm (AFSA) is a novel optimizing method. In this paper, AFSA was applied to function optimization and was compared with the above three methods. Experimental simulations show that the AFSA can find the global optimum more accurately.