在处理工程控制及设计中含有多参数，多约束的单目标优化问题时，为了获得更好的优化解，提出一种分类学习的粒子群优化算法。它根据每个粒子的函数适应值，将群体分为优势群体、中层群体和劣势群体三类，分别采取不同的学习方法和学习方向。优势群体继续保持自身的学习速度和学习方向；中层群体采取互相学习的策略；劣势群体采取加强向优势群体学习的策略。其优势在于不受函数连续、可导形式的制约。数值试验结果表明，相比于近年提出的一些改进粒子群算法，这种算法在处理含有单峰，多峰，离散，动态问题的函数时，具有良好的收敛性能。结合工程实例，在处理压力容器结构设计以及液压矫直机PID控制的参数优化问题时，此算法能够获得使系统性能更佳的参数组合。
To get the better solutions of the single objective engineering optimization problems, which have multi-parameters and multi-constraints, a novel particle swarm optimization algorithm is proposed with classification learning. The particle swarm is divided into three classes, i.e. better class, middle class and worse class. For each class, different learning methods and directions are used, respectively. For the better class, the learning speed and direction itself are remained. For the middle class, the interactive learning strategy is introduced. For the worse class, the learning direction to the better class is modified. Hence, the algorithm is not affected by the continuous and differentiable functions. It is illustrated that, by the numerical experiments, this algorithm has the better performance, to deal with the function which contains uni-modal, multi-modal discrete and dynamic problems, comparing with other improved particle swarm optimization algorithms. It is indicated by the engineering application examples that this algorithm can get the better parameters which can make the system to get the better performance, when dealing with structure design and hydraulic straightener PID controller problems.
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