Hagfish Optimization Algorithm

To address the problem that traditional swarm intelligence optimization algorithms generally rely on optimal individual guidance, visual assumptions, or explicit velocity models, a novel swarm intelligence optimization method—the hagfish optimization algorithm—is proposed. This algorithm is based on the non-visual survival behavior of deep-sea hagfish as a biological inspiration, constructing a novel optimization model from four aspects: information perception, memory mechanisms, group structure, and search dynamics. The hagfish optimization algorithm does not rely on optimal solution attraction, but instead achieves non-target-driven search through fitness change perception. Simultaneously, it introduces a mucus field memory mechanism in continuous space to achieve environment-level shared memory. At the group level, entanglement, breakage, and recombination mechanisms are designed to enhance the ability to escape local optima. At the dynamic level, a mucus-damped search model is constructed to suppress search oscillations. This algorithm significantly differs from existing classical swarm intelligence algorithms in its mechanism design, providing a new modeling approach for complex continuous optimization problems.