Abstract
The snow ablation optimizer (SAO) represents a novel metaheuristic algorithm tailored for addressing real-world optimization challenges. However, SAO exhibits certain drawbacks, including a tendency to get trapped in local optima, a sluggish convergence rate, and suboptimal performance on intricate multimodal function problems. Acknowledging these limitations, the enhanced snow ablation optimizer (ESAO) is introduced. In this paper, we elucidate the pivotal strategies for implementing ESAO, encompassing chaotic mapping and random opposition learning initialization, dynamic tangential flight strategy, adaptive inertia weight, and elite guidance boundary control strategy. To underscore the prowess of ESAO, we conducted extensive testing on 29 functions from the CEC2017 benchmark, 19 real-world engineering challenges derived from the CEC2020 benchmark functions, and UAV flight trajectory optimization. Furthermore, ESAO is compared with three categories of widely recognized algorithms: (1) classical algorithms such as PSO, HHO, and GWO; (2) recent algorithms like GOOSE, HEOA, Puma, and the original SAO; and (3) algorithmic variants including IGWO, IDBO, HPHHO, and E-WOA. The experimental outcomes reveal that ESAO surpasses the other 11 competitors in most cases, demonstrating remarkable convergence speed, stability, and accuracy. The superiority of ESAO is further confirmed by the Friedman mean ranking test and Wilcoxon rank sum test, underscoring its potential as a formidable metaheuristic algorithm.