A Fast Differential Evolution for Constrained Optimization Problems in Engineering Design

Abstract

A fast differential evolution (FDE) approach to solve several constrained engineering design optimization problems is proposed. In this approach, a new mutation strategy “DE/current-to-ppbest/bin” is proposed to get a balance between exploration and exploitation of the population. What’s more, a ranking based selection mechanism selects the promising individuals from the combination of parents and offspring to update the population. Experimental results on 5 instances extracted from engineering design show that FDE can acquire quite competitive performance. FDE is comparable to other state-of-the-art approaches in terms of solution quality. As for convergence speed, FDE is more fast, or at least comparable to, other state-of-the-art approaches. When the number of function evaluation is limited or the cost of function evaluation is expensive, FDE is a good choice.