The Archerfish Hunting Optimizer: a novel metaheuristic algorithm for global optimization

TL;DR

The paper introduces the Archerfish Hunting Optimizer (AHO), a new metaheuristic inspired by archerfish hunting behaviors, demonstrating superior performance in benchmark tests for global optimization.

Contribution

It presents a novel metaheuristic algorithm based on archerfish behaviors, validated through extensive benchmark comparisons and statistical analysis.

Findings

AHO outperforms 12 recent metaheuristics on benchmark functions.

AHO achieves better results on engineering design problems.

Statistical tests confirm AHO's superior performance.

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. Metaheuristics are 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 it the Archerfish Hunting Optimizer (AHO). We Perform two sorts of comparisons to validate the proposed algorithm's performance. First, AHO is compared to the 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. Second, the performance of AHO and 3 recent metaheuristic algorithms, is evaluated using five engineering design problems taken from the benchmark CEC 2020 for non-convex constrained optimization. The experimental results are evaluated using the Wilcoxon signed-rank and the Friedman tests. The statistical indicators illustrate that the Archerfish Hunting Optimizer has an excellent ability to accomplish higher performance in competition with the well-established optimizers.