The Archerfish Hunting Optimizer: a novel metaheuristic algorithm for global optimization
This paper offers an incremental metaheuristic algorithm for general global optimization problems, potentially benefiting researchers and practitioners seeking improved optimization techniques.
This paper introduces the Archerfish Hunting Optimizer (AHO), a new metaheuristic algorithm inspired by archerfish hunting behavior, designed for global optimization. It was tested against 12 recent metaheuristic algorithms on 10 unconstrained optimization functions and 3 recent algorithms on 5 constrained engineering design problems from the CEC 2020 benchmark, demonstrating superior performance based on statistical tests.
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.