A New Differential Evolution for Constrained Optimization Problems

Differential evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, nonlinear and multi-modal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary algorithm, but usually it takes large computational time for optimizing the computationally expensive objective function, therefore it is necessary to find a trade-off between convergence speed and robustness. For this purpose, in this paper, a new DE based on uniform design is presented for solving nonlinear constrained optimization problems. Constraints are handled by embodying them in an augmented Lagrangian function, where the penalty parameters and multipliers are adapted as the execution of the algorithm proceeds. The efficiency of the proposed methodology is illustrated by solving numerous constrained optimization problems that can be found in the literature