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.

Chẩn đoán độ cứng kết cấu hệ thanh bằng phương pháp cập nhật mô hình phần tử hữu hạn kết hợp thuật giải tiến hóa vi phân cải tiến

“…So sánh lân cận (NNC) là một kỹ thuật cho phép đánh giá sớm một cá thể mà không cần xác định hàm mục tiêu tương ứng. Kỹ thuật này lần đầu được Pham [27] đề xuất và sau đó đã được dùng trong một số thuật toán DE cải tiến để giải các bài toán tối ưu trong kỹ thuật [28][29][30][31]. Các bước thực hiện của NNC như sau:…”

Logistic Map and Exponential Scaling Factor-Based Differential Evolution

“…Advantageoftheproposedsystem,consideringlogisticmapisthat,forr=3.56,logisticmapperforms betterthanrandomnumberbecauselogisticmapisapolynomialmappingofdegree2,whichgives an idea of how a complex chaotic behavior can achieved from a non linear dynamic equation. (Anh, 2016).CaiandTian(2016)usedamodifiedmutationprocess,presentedanewparentselectionmethod whichwasabletodecidetheindividualsformutationbyutilizingtheinformationofthepopulation efficiently.ThisnewparentselectionselectionmethodwasappliedonbasicDEandDEvariantsand othervariousbenchmarkfunctions (Cai&Tian,2016).Anorthogonaldifferentialevolution(ODE) algorithmwasdevelopedbyYuXiangLei(2017)whichgeneratedtwovariants,firstoneactingonthe crossoveroperationandthesecondoneactingonselectionstage.AftercombiningtheODEwiththe basiconethemodifiedalgorithmwasexecutedindifferentunimodals,multimodalsandcomposition functionswhichgavecompetitiveresults (Leietal.,2017).Amodelbasedonshrinkingspaceand adaptivetradeoffamodifieddifferentialevolutionalgorithmwasdesignedbyChunming(2017)and variousoffspringaregeneratedwiththehelpofthismodifiedalgorithm.Withconstantaccuracy, thatmodifiedalgorithmcanhandlemultipleconstraints.Themodifiedalgorithmintroducedthree mutationstrategies.Differentbenchmarkproblemsandvariousengineeringapplicationswereusedto verifytheoutcomes (Fuetal.,2017). Knobloch,MlýnekandSrb(2017)presentedglobalconvergence of differential algorithm.…”