AUTOMOTIVE PARTS' LOADING OPTIMIZATION BASED ON IMPROVED QUADRATIC PARTICLE SWARM OPTIMIZATION

摘要

The present article designed a genetic quadratic particle swarm optimization (GQPSO). Aiming at the low particle diversity at the early searching stage of quadratic particle swarm optimization (QPSO), the method adopts mutation and exchanging and regenerating mechanisms from genetic algorithm so as to avoid premature convergence and improves optimization. Meanwhile, the present article gave a comprehensive consideration to decision elements such as cost, resources, and service in the process of automotive parts' logistics, transportation, and loading; a model of automotive parts' logistics, transportation, and loading optimization was set up. GQPSO was introduced for solutions. Simulation examples show that GQPSO improves the computational efficiency, significantly, and there is a higher probability of searching the global optimum. It provides optimization method for the automobile parts' logistics and transportation plans.