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

说明：用小生境遗传算法优化一元函数，包括主程序和各子程序，直接运行出结果。-Using niche genetic algorithm to optimize the function, including the main program and each subroutine, run directly from the results.

利用粒子群算法进行优化调度，以经济性为目标函数，完全可以运行(The particle swarm optimization algorithm is used to optimize the scheduling, which takes the economy as the objective function and can run completely)