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中文摘要：
为了学习这个问题，当分析高维的复杂优化问题时，那个粒子群优化(PSO ) 算法能容易套住进本地机制，用在更多的粒子的反复的进程的信息的优化计算被分析，粒子群算法的最佳的系统被改进。扩大粒子群优化算法(EPSO ) 被建议。能控制选择的纹理粗糙、有细密纹理的标准被给保证算法的集中。二个标准在随机的概率的状况下面考虑了参数选择机制。由采用 MATLAB7.1，扩大粒子群优化算法在力量 project scheduling 铺平的资源被表明。EPSO 与基因算法(GA ) 和普通 PSO 相比，结果显示资源铺平的客观功能的变化被 7.9% 减少， 18.2% 分别地，证明有效性和 EPSO 的更强壮的全球集中能力。
英文摘要：
In order to study the problem that particle swarm optimization （PSO） algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm （EPSO） was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm （GA） and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.
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