Scissor Insect Optimization Algorithm

Swarm intelligence optimization algorithms have shown broad application potential in complex continuous optimization problems. This paper proposes a novel swarm intelligence optimization algorithm—the Scissor Insect Optimization Algorithm (SIOA), whose core idea is derived from the hunting behavior of scissor insects in nature. The algorithm simulates the scissor insect's pincer predation mechanism, introducing multi-scale search, adaptive pincer convergence, neighborhood cooperation, and energy adjustment mechanisms to achieve a balance between global search and local fine-tuning. This paper presents the algorithm's mathematical model, iterative formulas, and theoretical analysis, proving its good global search capability, local convergence accuracy, and resistance to premature convergence. This research provides new ideas for the development of swarm intelligence optimization algorithms and offers feasible methods for engineering and scientific optimization problems.