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

多核极限学习器，是一种前馈神经网络，能逼近任意连续目标函数或分类任务重的任何复杂决策边界(Multi-kernel limit learner is a feedforward neural network, which can approach any complex decision boundary of any continuous objective function or classification task)