Box Turtle Optimization Algorithm

Swarm intelligence optimization algorithms solve complex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes a box turtle optimization algorithm based on the behavioral characteristics of box turtles—BTOA (Box Turtle Optimization Algorithm). By simulating the box turtle's foraging, migration, and defense behaviors, and combining energy control, multi-level contraction mechanisms, dynamic step size adjustment, and environmental sensitivity judgment, the algorithm effectively combines global exploration with local exploitation. The algorithm enhances search efficiency and robustness through information sharing among the group and neighbor fitness analysis. This paper elaborates on the algorithm modeling, iterative process, characteristics, and theoretical analysis of BTOA, providing an effective and novel swarm intelligence optimization method for complex high-dimensional optimization problems.