AN EFFICIENT DIFFERENTIAL EVOLUTION FOR CONSTRAINED ENGINEERING DESIGN OPTIMIZATION

Many variants of Differential Evolution (DE) algorithms exist in literature to solve Engineering Design Problems. However, the performance of DE is highly affected by the inappropriate choice of its operators like mutation and crossover. Moreover, in general practice, DE does not employ any strategy of memorizing the so-far-best results obtained in the initial part of the previous cycle. In this paper a ‘Memory based DE (MBDE)' propose which having two ‘swarm operators'. These operators are based on the personal best and global best mechanism of particle swarm optimization (PSO). The feasibility of the proposed algorithm is tested on constrained engineering design problems. Result analysis reveals competency of the proposed algorithm.