Constrained optimization using two-phase evolutionary programming

A hybrid of evolutionary programming (EP) and a deterministic optimization procedure applied to a series of nonlinear optimization problems has been proved to be useful when addressing heavily constrained optimization problems in terms of computational efficiency and solution accuracy. The hybrid EP, however, can be applied only if the mathematical form of the objective function to be minimized/maximized and its gradient are known. To remove such restrictions, a two-phase evolutionary programming method is proposed. The first phase uses the standard EP, while the second phase uses the elitist EP with deterministic ranking strategy. 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 comparisons among variants of two-phase EP indicate that the proposed two-phase EP achieves an exact solution with less computation time without reducing convergence stability