Indian Star Tortoise Optimization: A Metaheuristic with Directional Memory and Irreversible Reachability

To address the common problems of traditional metaheuristic optimization algorithms, such as reliance on random perturbations, repeated revisits to the search space, and highly reversible search trajectories, this paper proposes a novel swarm optimization method—the Indian Star Turtle Optimization Algorithm. This algorithm no longer treats the search process as a random walk within a fixed reachable space, but rather models the optimization as a monotonically shrinking reachability pruning process over time. By introducing star-pattern reachable sectors, an irreversible directional deactivation mechanism, and star-shell inertial constraints, the algorithm causes irreversible changes in the topology of the search space during evolution, thus significantly suppressing ineffective oscillations and repeated explorations. This paper provides a systematic model of the algorithm from the perspective of dynamical systems and probabilistic processes, revealing its intrinsic mechanism as an irreversible search dynamic, and discusses its potential advantages in complex non-convex optimization problems.