Social cognitive optimization algorithm for class of non-differentiable multi-objective optimization problems
求解一类不可微多目标优化问题的社会认知算法*

To solve a class of non-differentiable multi-objective optimization problems, this paper proposed a new method called maximum-entropy social cognitive optimization algorithm. First, used the maximum-entropy function, transformed the constrained non-differentiable multi-objective optimization problem to the approximation unconstrained differentiable optimization problem, then used the social cognitive optimization algorithm to solve this problem. The algorithm was based on social cognitive theory, through a series of learning agents to simulate human social and intelligent thereby completing the optimization of the target. Used two examples to demonstrate the validity of the method and compared the results with the ones of other methods. It shows that the proposed method is more accurate and effective.