Saola Optimization Algorithm (SOA): A Nature-inspired Metaheuristic for Efficient Global and Local Search in Complex Optimization Landscapes

A novel, nature-inspired metaheuristic, the Saola Optimization Algorithm (SOA), grounded in the distinctive ecological behaviour of the Saola, including habitat-based exploration, rapid threat avoidance, and selective foraging, is presented.SOA employs a phased update strategy that adaptively balances global exploration and local exploitation, enabling efficient identification of promising solution regions while avoiding premature convergence and local optima.The algorithm has been rigorously evaluated on a comprehensive set of benchmark problems encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal scenarios, and its performance is compared against nine state-of-the-art metaheuristic algorithms.Results of evaluation demonstrate that SOA consistently achieves superior convergence speed, solution accuracy, robustness, and population diversity across all tested problems.Convergence curve analyses further confirm its rapid initial decline and early stabilization, reflecting an effective trade-off between exploration and exploitation.A key advantage of SOA lies in its parameterfree, ecologically motivated design, which provides adaptive and reliable optimization without the need for manual tuning.The algorithm's unique combination of probabilistic habitat exploration, threat-driven global displacement, and selective local refinement offers a versatile framework for addressing complex optimization landscapes.SOA's robust performance suggests a strong potential for application in real-world, as well as for extension to multi-objective, constrained, and hybrid optimization scenarios.Overall, the Saola Optimization Algorithm represents a highperforming, theoretically grounded, and generalizable metaheuristic capable of efficiently solving diverse and challenging optimization problems.