多目标非线性约束的粒子群算法，采用灰度理论，混沌理论，动态惩罚函数，可针对任何复杂函数进行优化，效果很好-Nonlinear constrained multi-objective particle swarm algorithm, using gray theory, chaos theory, dynamic penalty function can be optimized for any complex function, the effect is very good

多目标粒子群算法，最优的解决方案，使用一个外部文件管理，拥塞机制筛选，灰色设计验过滤。-Multi-objective particle swarm algorithm, the optimal solution using an external file management, congestion mechanism screening, gray design posteriori filtering.

一个粒子群多目标算法，带有权重自适应，可以画出相应的pareto前沿(A particle swarm multi-objective algorithm, with weight adaptive, can draw corresponding Pareto frontiers.)