A hybrid approach for high-dimensional optimization: Combining particle swarm optimization with mechanisms in neuro-endocrine-immune systems

摘要

Particle swarm optimization (PSO) tends to fall into local optimum during the high-dimensional optimization process To address this limitation, a hybrid optimization approach by combining PSO with mechanisms in neuro-endocrine-immune systems (NEI-PSO) is proposed. The NEI-PSO includes a nervous guidance unit, an endocrine regulation unit, and an immune orientation unit. The nervous guidance unit and the immune orientation unit are designed based on the nervous system guidance mechanism and the immune system orientation mechanism respectively. Through the joint effect of these two units, the update mode of particle movement is changed; as a result, the global search ability of the NEI-PSO can be improved. The endocrine regulation unit changes the learning factor based on the hormone regulation law of the endocrine system, and in turn improves the optimization convergence speed of the proposed approach. In this paper, the NEI-PSO is evaluated using eight high-dimensional benchmark functions and a real-world high-dimensional optimization application for a non-Pieper six-axis robot. The results demonstrate that the proposed NEI-PSO approach has prominent advantages in search accuracy, convergence ability, and stability, compared to some existing optimization approaches.