A Simplified Fish School Search Algorithm for Continuous Single-Objective Optimization

The Fish School Search (FSS) algorithm is a metaheuristic known for its distinctive exploration and exploitation operators and cumulative success representation approach. Despite its success across various problem domains, the FSS presents issues due to its high number of parameters, making its performance susceptible to improper parameterization. Additionally, the interplay between its operators requires a sequential execution in a specific order, requiring two fitness evaluations per iteration for each individual. This operator’s intricacy and the number of fitness evaluations pose the issue of costly fitness functions and inhibit parallelization. To address these challenges, this paper proposes a Simplified Fish School Search (SFSS) algorithm that preserves the core features of the original FSS while redesigning the fish movement operators and introducing a new turbulence mechanism to enhance population diversity and robustness against stagnation. The SFSS also reduces the number of fitness evaluations per iteration and minimizes the algorithm’s parameter set. Computational experiments were conducted using a benchmark suite from the CEC 2017 competition to compare the SFSS with the traditional FSS and five other well-known metaheuristics. The SFSS outperformed the FSS in 84% of the problems and achieved the best results among all algorithms in 10 of the 26 problems.