Abstract:

In order to solve constrained optimization problems with higher accuracy and faster computing speed, several improvements are raised on particle swarm optimization(PSO) and its computing method. Solutions’ diversification mechanism is applied in PSO to improve its global optimization ability: decision space is divided into multiple searching subspaces, while multi-subswarms are created according to those searching subspaces, and multi-subswarms are searched independently to get solutions’ diversification among subswarms; ordinary particles is replaced by quantum particles in PSO, while associated particles that follow globular distribution is vested in each quantum particle, which could improve solutions’ diversification in subswarms. Running speed of the improved PSO is increased via parallel computing: Parallel computing flow of the improved PSO is analyzed based on the private cloud platform and the algorithm for the flow is programmed based on MapReduce. The experimental results show that the proposed method has higher accuracy solutions and stability, and the performance and computing speed is exponentially improved.