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. | Vietnam Journal of Mechanics, VAST, Vol. 38, No. 2 (2016), pp. 89 – 101 DOI: ENGINEERING OPTIMIZATION BY CONSTRAINED DIFFERENTIAL EVOLUTION WITH NEAREST NEIGHBOR COMPARISON Pham Hoang Anh National University of Civil Engineering, Hanoi, Vietnam E-mail: Received July 27, 2015 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. Keywords: Engineering optimization, differential evolution, ε constrained method, nearest neighbor comparison. 1. INTRODUCTION Engineering optimization problems arising from modern engineering design process often involve inequality and/or equality constraints. Most of these constrained optimization problems (COPs) are complex and difficult to solve by traditional optimization techniques [1]. Evolutionary algorithms (EAs) for the COPs have received considerable attention and have been successfully applied in many real applications [2–4]. Among different EAs, differential evolution (DE) [5] is considered as one of the most efficient algorithm and suitable for various engineering problems. The advantage of DE is that it has simple structure, requires few control parameters and highly supports parallel computation [6]. Together with the constraint-handling techniques, DE has .
