Engineering optimization by constrained differential evolution with nearest neighbor comparison

Abstract

It has been proposed to utilize nearest neighbor  comparison to reduce the number of function evaluations in unconstrained  optimization. The nearest neighbor comparison omits the function evaluation  of a point when the comparison can be judged by its nearest point in the  search population. In this paper, a constrained differential evolution (DE)  algorithm is proposed by combining the ε constrained method to  handle constraints with the nearest neighbor comparison method. The  algorithm is tested using five benchmark engineering design problems and the  results indicate that the proposed DE algorithm is able to find good results  in a much smaller number of objective function evaluations than conventional  DE and it is competitive to other state-of-the-art DE variants.