The Archerfish Hunting Optimizer: A Novel Metaheuristic Algorithm for Global Optimization

Abstract

Global optimization solves real-world problems numerically or analytically by minimizing their objective functions. Most of the analytical algorithms are greedy and computationally intractable (Gonzalez in Handbook of approximation algorithms and metaheuristics: contemporary and emerging applications, vol. 2. CRC Press, Boca Raton, 2018). Metaheuristics are generally nature-inspired optimization algorithms. They numerically find a near-optimal solution for optimization problems in a reasonable amount of time. We propose a novel metaheuristic algorithm for global optimization. It is based on the shooting and jumping behaviors of the archerfish for hunting aerial insects. We name our proposed algorithm the archerfish hunting optimizer (AHO). The AHO algorithm has two parameters (the swapping angle and the attractiveness rate) to set. We execute the AHO algorithm using five different values for each parameter. In all, we perform 25 simulations for four distinct values of the search space dimension (i.e., 5, 10, 15, and 20). We run the Friedman test to determine the best values of parameters for each dimension. We perform three different comparisons to validate the proposed algorithm’s performance. First, AHO is compared to 12 recent metaheuristic algorithms (the accepted algorithms for the 2020’s competition on single-objective bound-constrained numerical optimization) on ten test functions of the benchmark CEC 2020 for unconstrained optimization. The experimental results are evaluated using the Wilcoxon signed-rank test. Experimental outcomes show that the AHO algorithm, in terms of robustness, convergence, and quality of the obtained solution, is significantly competitive compared to state-of-the-art methods. Second, the performance of AHO and three recent metaheuristic algorithms is evaluated using five engineering design problems taken from the benchmark CEC 2020 for non-convex constrained optimization. The obtained results are ranked using the ranking scheme detailed in the corresponding paper, and the obtained ranks illustrate that AHO is very competitive when opposed to the considered algorithms. Finally, the performance of AHO in solving five engineering design problems is assessed and compared to several well-established state-of-the-art algorithms. We analyzed the obtained numerical results in detail. These results show that the AHO algorithm is significantly better than, or at least comparable to the considered algorithms with very efficient performance in solving many optimization problems. The statistical indicators illustrate that the AHO algorithm has a high ability to significantly outperform the well-established optimizers.