Engineering optimization by constrained differential evolution with nearest neighbor comparison
Keywords:engineering optimization, differential evolution, ε constrained method, nearest neighbor comparison
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.
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