2021/02/03 by Farouq Zitouni, Zitouni, Farouq, Saad Harous +5
Computer Science · Mathematics · #90C59 #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #G.1.6 #I.1.2 #I.2.8 #Metaheuristic Optimization Algorithms Research
paper · pdf · doi:10.48550/arxiv.2102.02134
openalex publication_date 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Global optimization solves real-world problems numerically or analytically by\nminimizing their objective functions. Most of the analytical algorithms are\ngreedy and computationally intractable. Metaheuristics are nature-inspired\noptimization algorithms. They numerically find a near-optimal solution for\noptimization problems in a reasonable amount of time. We propose a novel\nmetaheuristic algorithm for global optimization. It is based on the shooting\nand jumping behaviors of the archerfish for hunting aerial insects. We name it\nthe Archerfish Hunting Optimizer (AHO). We Perform two sorts of comparisons to\nvalidate the proposed algorithm's performance. First, AHO is compared to the 12\nrecent metaheuristic algorithms (the accepted algorithms for the 2020's\ncompetition on single objective bound-constrained numerical optimization) on\nten test functions of the benchmark CEC 2020 for unconstrained optimization.\nSecond, the performance of AHO and 3 recent metaheuristic algorithms, is\nevaluated using five engineering design problems taken from the benchmark CEC\n2020 for non-convex constrained optimization. The experimental results are\nevaluated using the Wilcoxon signed-rank and the Friedman tests. The\nstatistical indicators illustrate that the Archerfish Hunting Optimizer has an\nexcellent ability to accomplish higher performance in competition with the\nwell-established optimizers.\n