Solenodon Optimization Algorithm (SOA): A Bio-inspired Metaheuristic for Efficient Global Optimization

Optimization problems arise across science and engineering and often exhibit nonlinearity, multimodality, and high dimensionality, making classical deterministic techniques ineffective.Metaheuristic algorithms have, therefore, become powerful alternatives due to their flexibility, robustness, and problem-independent nature.A new bio-inspired metaheuristic, termed the Solenodon Optimization Algorithm (SOA), motivated by the unique nocturnal foraging and hunting behaviors of the solenodon, is proposed.The algorithmic design is grounded in a rigorous mapping between real biological behaviors and mathematical operators governing the search process.SOA employs a two-phase search strategy.The exploration phase simulates wide-area nocturnal movement combined with olfactorydriven detection, enabling agents to traverse large regions of the search space while being adaptively guided toward promising areas.Sudden directional changes are incorporated to enhance population diversity and avoid premature convergence.The exploitation phase is inspired by the solenodon's precision spot-digging behavior and performs localized, fine-grained search with progressively decreasing step sizes to intensify solution refinement.The proposed algorithm is evaluated on a comprehensive set of benchmark optimization problems, including unimodal, multimodal, and fixed-dimensional test functions.Simulation results demonstrate that SOA achieves competitive or superior performance compared with several well-established metaheuristic algorithms in terms of solution accuracy, convergence speed, and robustness.The results confirm that the biologically inspired balance between global exploration and local exploitation enables SOA to effectively handle complex optimization landscapes efficiently.