Star Tortoise Optimization Algorithm

Swarm intelligence optimization algorithms are a class of methods that solve complex problems by simulating the collective behavior of biological groups in nature. In recent years, they have been widely applied in continuous optimization, combinatorial optimization, and constrained optimization. However, traditional swarm intelligence algorithms still suffer from local optimum traps and insufficient early convergence speed when solving high-dimensional, multi-modal functions. To address this issue, this paper proposes a novel swarm intelligence optimization algorithm—the Star Turtle Optimization Algorithm (STOA). Inspired by the navigation behavior and energy management mechanism of the star turtle, this algorithm combines global exploration, local search, and adaptive step size adjustment mechanisms to achieve an effective combination of early search space coverage and later fine-grained convergence. The algorithm design includes chaotic initialization, multi-star domain navigation, cooperative guidance coupled with random perturbation, and an energy stratification mechanism. The algorithm process is systematically described using mathematical formulas. Theoretical analysis shows that STOA possesses good global search capabilities, local fine-grained optimization capabilities, and adaptive characteristics, and can be widely applied to continuous, multi-modal, and constrained optimization problems.