Two-phase evolutionary programming for constrained numerical optimization

In this paper, two-phase evolutionary programming (TPEP) is proposed, which does not require gradient information of the objective function and constraints. The first phase uses the standard EP and the EP formulation of the augmented Lagrangian method is employed in the second phase. Using Lagrange multipliers and gradually putting emphasis on violated constraints in the objective function whenever the best solution does not fulfill the constraints, the trial solutions are driven to the optimal point where all constraints are satisfied. The simulation results indicate that TPEP achieves an exact solution with less computation time without reducing convergence stability