A Novel Two-Level Evolutionary Algorithm for Solving Constrained Function Optimization

In this paper, a novel Two-Level Evolutionary Algorithm (TLEA) for solving function optimization problems with inequality constraints is proposed. It develops several new concepts and two new operator, namely Big Mutation Operator and Reinitialization, and introduce the Guo´s crossover operator as well, to improve the convergence, and uses a two-level algorithm framework, i.e., it uses the first level to fast locate the domain that the global optimum exists, and uses the second level to convergence to the global optimum. The simulation results on some typical test problems show that the algorithm proposed in this paper is better than existing evolutionary algorithm in the accuracy of solutions and efficiency of convergence.