Tamandua Optimization Algorithm

With the increasing complexity of modern engineering optimization problems, traditional optimization algorithms often get stuck in local optima when dealing with high-dimensional, multimodal, and non-convex problems. Animal behavior in nature provides rich inspiration for optimization algorithms, especially the marsupial anteater (Tamandua), whose flexible predation behavior and strong adaptability to the environment offer new ideas for continuous optimization problems. This paper proposes an optimization algorithm based on the behavior of the marsupial anteater, named the Tamandua Optimization Algorithm (TOA). Inspired by the marsupial anteater's predation strategy, global migration behavior, and energy allocation mechanism, this algorithm achieves efficient optimization search through a combination of multi-scale olfactory perception, local fine-grained search and global exploration, historical trajectory memory, and group collaborative information flow. This paper provides a detailed mathematical description of the algorithm modeling, iterative update mechanism, search strategy, and energy adjustment, providing new theoretical methods and application foundations for optimization problems.