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中文摘要：
综合考虑汽车零配件物流运输配载过程中成本、资源及服务质量等决策要素，建立了汽车零配件配载优化模型。引入二次粒子群算法对该问题进行求解，并针对该算法在搜索早期粒子多样性低的缺点，提出了改进二次粒子群优化算法，它采用遗传算法的变异思想和互换更新机制来提高种群的多样性，以避免过早收敛和改进优化效果。仿真实例表明，与原算法相比，改进后算法的计算效率显著提高，且搜索到全局最优解的概率也更高。
英文摘要：
An optimization model for the cargo allocation of auto parts is set up with a comprehensive consideration of the decision elements, including costs, resources and service, in the process of logistics, transportation and cargo allocation for auto parts. Quadratic particle swarm optimization （QPSO） algorithm is introduced to solve the problem, but in view of the defect of low particle diversity at the early searching stage, an improved quadratic particle swarm optimization algorithm GQPSO is put forward, which adopts the variation idea and crossover / mutation mechanisms of genetic algorithm to increase the diversity of population for avoiding premature convergence and improves optimization results. Simulation examples show that compared with original algorithm, the improved algorithm GQPSO has much higher computing efficiency and higher probability of finding global optimum.
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