An Improved Differential Evolution Algorithm for Solving Constrained Optimization Problems

In this paper, an improved evolutionary differential algorithm named MDE is proposed to solve the classical constrained optimization problem. This method is based on multi-parent crossover, which generates offspring based on the center individual and three randomly selected individuals. The offspring created by this crossover scheme are closer to the feasible region. To deal with the solutions in the boundaries of feasible region, we apply a boundary search strategy. To handle constraints, we employ a feasible solution preferred rule (an individual with less constraint violations is better). To verify the performance of our approach, we test it on 13 well-known constrained benchmark optimization problems. Simulation results and comparisons demonstrate that our algorithm can effectively deal with constraints and achieves better feasible solutions. Additionally, we apply the algorithm to solve four real-world applications, including welded beam design optimization problem, pressure vessel design optimization problem, tension/compression spring design optimization problem and speed reducer design optimization problem. Simulation results demonstrate the effectiveness of our algorithm.