Application of HGPSOA in Electric Power System Material Purchase and Storage Optimization

Abstract

Inventory control is an important aspect of logistics management in modern enterprise. This paper establishes a material purchase and storage optimization model for electric power plants to minimize the cost based on its characteristic of raw material stock. Then hybrid genetic particle swarm optimization algorithm (HGPSOA) is used to solve the optimization model. The algorithm combines the evolution idea of genetic algorithm (GA) with population intellectual technique of particle swarm optimization (PSO) algorithm, and displays the more excellent searching performance. During searching process, some individuals are iterated by PSO, the others follow the selection, crossover and mutation of GA, and the whole population information is shared by each agent. Simultaneously, it adopts the adaptive parameters mechanism and better fitness individuals surviving rules to evolve the population. Finally, the algorithm is applied to the material purchase and storage optimization model. Example shows that HGPSOA displays more prominent advantages both in the solving performance and efficiency.