New method for solving a class of dynamic nonlinear constrained optimization problems

Dynamic nonlinear constrained optimization problems (DNCOP) is a class of complex dynamic optimization problems, the difficult to solve the DNCOP is how to do with the constraint and its time(environment) variance. In this paper, a new multi-objective evolutionary algorithm for solving a class of nonlinear constrained optimization problem which the time (environment) variance is defined in discrete space is given. First, a new dynamic entropy function based on the constraint conditions of dynamic nonlinear constrained optimization problem is given. Then using the new entropy function, the original dynamic nonlinear constrained optimization problem is transformed into a bi-objective dynamic optimization problem. Furthermore, a new crossover operator and a mutation operator with local search were designed. Based on these, a new multi objective evolutionary algorithm is proposed. The computer simulations are made on two dynamic nonlinear constrained optimization problems, and the results indicate the proposed algorithm is effective.