Salamander Optimization Algorithm: A New Bio-Inspired Approach for Solving Optimization Problems

Abstract

In this study, a novel nature-inspired metaheuristic algorithm, the Salamander Optimization Algorithm (SOA), is introduced for complex optimization tasks. SOA is designed based on the remarkable biological characteristics of salamanders, including their regenerative abilities, adaptability to diverse environments, and efficient movement strategies. These natural behaviors are mathematically modeled to establish a powerful optimization framework, ensuring a dynamic balance between exploration and exploitation. The performance of SOA is extensively evaluated on 52 benchmark functions, encompassing unimodal, multimodal, and composite functions. To validate its competitiveness, SOA is benchmarked against twelve state-of-the-art metaheuristic algorithms. Comprehensive statistical analyses confirm the algorithm’s superior search efficiency, robustness, and convergence characteristics, consistently outperforming competing methods in both accuracy and solution quality. The findings underscore SOA’s capability to navigate complex search landscapes effectively, avoiding local optima while maintaining computational efficiency. The results establish SOA as a highly effective optimization technique, with potential applications in diverse scientific and engineering domains. This research not only presents an innovative optimization approach but also lays the groundwork for future developments in adaptive and parameter-free metaheuristic methodologies.