Multi-Level and Multi-Swarm Particle Swarm Optimization Algorithm

Abstract

Inspired by the mechanism of batch processing systems, this paper proposes a multi-level and multi-swarm particle swarm optimization (MMPSO) algorithm to alleviate the performance degradation caused by imbalances in exploration and exploitation within the swarm. The algorithm constrains the learning behavior of different swarms by dividing the swarm into multi-level and multi-swarm. Swarms of different levels are grouped into various to perform different tasks. The small-scale in top-level swarm is responsible for exploiting the global optimal position. The large-scale in bottom-level swarm uses the Levy flight strategy based on the improvement rate to explore unknown areas, effectively improving the exploration ability. The middle-level swarm is divided into multi-swarm to explore and exploit local locations and design an internal and external comprehensive learning strategy. A lifting mechanism is proposed to improve the information exchangeability of swarms at different levels. Experimental results on the 30-D of the CEC2017 benchmark optimization problems show that the MMPSO algorithm exhibits the best convergence accuracy on most benchmark functions due to the balance of exploration and exploitation. Furthermore, the algorithm combine with BP Neural Networks (BPNN) shows the best convergence accuracy on the application problem of developing force-field parameters (FFPs).