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中文摘要：
经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的有效方法．目前，粒子群算法（particle swarm optimization，PSO）已经成为优化领域中的一个重要的优化工具，其应用在很多优化问题中都可以见到．虽然粒子群算法的应用范围已经十分广泛，但是关于应用其求解多级生产批量计划问题（multilevel lot—sizing problem，MLLS）的文章并不多见．文章提出结合遗传算法（genetic algorithm，GA）变异算子的混合粒子群优化算法（hybrid particle swarm optimization，HPSO）求解无能力约束装配结构MILS问题．通过实验验证了算法的可行性和有效性．
英文摘要：
The classical Particle Swarm Optimization （PSO） algorithm is a powerful method to find the minimums of numerical functions on a continuous definition domain. It has been a very important optimization tool in many research fields. So far, papers on the application of PSO algorithm to multilevel lot-sizing （MLLS） problems can not be seen often. In view of this, a PSO algorithm combined with the mutation operator of Ge- netic algorithm （GA） is come up with to solve MLLS problems. Our aim is to expand the application scope of PSO algorithm. Experiments showed the feasibility and credibility of this algorithm.
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