  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.
