Hillstar Optimization Algorithm

Swarm intelligence optimization algorithms are a class of methods that solve complex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes a novel swarm intelligence optimization algorithm—the Hillstar Optimization Algorithm (HSOA), inspired by the foraging, singing, and social behaviors of myna birds in their natural environment. HSOA achieves a balance between global search and local exploitation by simulating the myna bird's multiple behavioral strategies, group cooperation mechanisms, and experience memory mechanisms. The algorithm introduces random foraging behavior, local concentrated search behavior, defensive escape behavior, contrastive learning mechanisms, and experience base memory mechanisms, which can effectively improve the convergence speed and global search capability of the algorithm in high-dimensional complex optimization problems. This paper theoretically analyzes the convergence, swarm intelligence, and adaptive cooperation capabilities of the algorithm, and provides detailed mathematical formulas describing the algorithm's update rules, providing a theoretical foundation for subsequent experiments and applications.