Giant Squid Optimization Algorithm

This paper proposes a swarm intelligence optimization algorithm based on the hunting behavior of giant deep-sea squid—the Giant Squid Optimization Algorithm (GSOA). This algorithm simulates the behavior of giant squid in the deep sea, utilizing jet propulsion, tentacled hunting, and swarm cooperation to find prey, transforming the optimization problem into a deep-sea search and hunting process. By introducing a dynamic energy-velocity adjustment mechanism, adaptive tentacle weight adjustment, a deep-sea perturbation mechanism, dynamic switching of individual roles, and a memory mechanism, GSOA can achieve a balance between global search and local exploitation in continuous, high-dimensional, and multi-modal optimization problems, exhibiting strong resistance to local optima. This paper details the algorithm modeling, iterative process, mathematical formulas, characteristic analysis, and potential applications, providing a novel approach to swarm intelligence optimization algorithms.