Mert PaldrakM. Fatih TasgetirenPonnuthurai Nagaratnam SuganthanQuanke Pan2025-10-062016978150900622910.1109/CEC.2016.7744115https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008244093&doi=10.1109%2FCEC.2016.7744115&partnerID=40&md5=0bc4a40dfd1573f8c844df800cd4ba76https://gcris.yasar.edu.tr/handle/123456789/9759In this paper an ensemble of differential evolution algorithms based on a variable neighborhood search algorithm (EDE-VNS) is proposed so as to solve the constrained real parameter-optimization problems. The performance of DE algorithms heavily depends on the mutation strategies crossover operators and control parameters employed. The proposed EDE-VNS algorithm employs multiple mutation operators and control parameters in its VNS loops to enhance the solution quality. In addition we utilize opposition-based learning (OBL) to take advantages of opposite solutions to find a candidate solution which might be close to the global optimum. In addition we also present an idea of injecting some good dimensional values from promising areas in the population to the trial individual through the injection procedure. The computational results show that the EDE-VNS algorithm is very competitive to some of the best performing algorithms from the literature. © 2017 Elsevier B.V. All rights reserved.EnglishConstraint Handling, Ensemble Of Differential Evolution, Real Parameter Optimization, Variable Neighborhood Search, Constrained Optimization, Optimization, Parameter Estimation, Quality Control, Computational Results, Constrained Real-parameter Optimization, Constraint Handling, Differential Evolution, Differential Evolution Algorithms, Opposition-based Learning, Real-parameter Optimization, Variable Neighborhood Search, Evolutionary AlgorithmsConstrained optimization, Optimization, Parameter estimation, Quality control, Computational results, Constrained real-parameter optimization, Constraint handling, Differential Evolution, Differential evolution algorithms, Opposition-based learning, Real-parameter optimization, Variable neighborhood search, Evolutionary algorithmsConference Object