An Enhanced Fish School Search Algorithm

Abstract

Fish School Search (FSS) is swarm-based optimizer that excels on multimodal search problems, but presents some drawbacks, such as the necessity to proper define the step used in some operators and the need to evaluate the fitness function twice per fish per iteration. This paper presents a simpler and enhanced version of the FSS, that features three advantages over the original FSS: high exploitation capability, just one fitness evaluation per fish per iteration and easy implementation. We name this novel version as FSS-II. Our proposal was compared to the FSS and the two most used PSO variations in terms velocity of convergence and robustness in six benchmark functions. FSS-II outperformed the other approaches in most of cases.