Vampire Squid Optimization Algorithm

With the widespread application of complex optimization problems in engineering, computer science, and management science, swarm intelligence optimization algorithms have attracted widespread attention due to their superiority in high-dimensional, nonlinear, and multi-extremum problems. This paper proposes a novel swarm intelligence optimization algorithm—the Vampire Squid Optimization Algorithm (VSOA)—inspired by the vampire squid (Vampyroteuthis infernalis) living in the low-oxygen environment of the deep sea. The algorithm achieves an effective balance between global search and local exploitation by simulating the vampire squid's low-energy drift, tentacle sensing, multi-stage contraction and expansion strategies, and energy sharing mechanism. The paper details the algorithm's mathematical modeling, iterative mechanism, parameter meaning, and characteristic analysis, providing a new and efficient solution method for complex optimization problems.