Small-Headed Dolphin Optimization Algorithm

In recent years, swarm intelligence optimization algorithms have been widely applied in problems such as function optimization, combinatorial optimization, and multi-objective optimization. However, traditional swarm intelligence algorithms have certain limitations in terms of search space shrinkage, premature convergence, and insufficient local exploitation capabilities. This paper proposes a novel swarm intelligence optimization algorithm—the Small-Headed Dolphin Optimization Algorithm (SHDOA), inspired by the predation and cooperative behavior of the vaquita dolphin. This algorithm achieves a balance between rapid global exploration and refined local exploitation while maintaining swarm diversity by introducing a spiral jumping strategy, a non-uniform cooperation mechanism, memory-enhanced local search, and a dynamic multi-sensory mechanism. This paper details the algorithm modeling, iterative process, mathematical formula derivation, and mechanism analysis, and provides a theoretical explanation of the algorithm's characteristics. The results show that SHDOA possesses strong global search capabilities, high local exploitation accuracy, and dynamic adaptability, providing a new solution for complex optimization problems.