Rammohan MallipeddiPonnuthurai Nagaratnam SuganthanQuanke PanM. Fatih TasgetirenMallipeddi, R.Suganthan, P. N.Tasgetiren, M. F.Pan, Q. K.2025-10-062011156849461568-49461872-968110.1016/j.asoc.2010.04.0242-s2.0-78650872465https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650872465&doi=10.1016%2Fj.asoc.2010.04.024&partnerID=40&md5=1ef894a59e497995baf81db2563c315fhttps://gcris.yasar.edu.tr/handle/123456789/10243https://doi.org/10.1016/j.asoc.2010.04.024Differential evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However the performance of DE is sensitive to the choice of the mutation strategy and associated control parameters. Thus to obtain optimal performance time-consuming parameter tuning is necessary. Different mutation strategies with different parameter settings can be appropriate during different stages of the evolution. In this paper we propose to employ an ensemble of mutation strategies and control parameters with the DE (EPSDE). In EPSDE a pool of distinct mutation strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE is evaluated on a set of bound-constrained problems and is compared with conventional DE and several state-of-the-art parameter adaptive DE variants. © 2010 Elsevier B.V. All rights reserved. © 2011 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDifferential Evolution, Ensemble, Global Optimization, Mutation Strategy Adaptation, Parameter Adaptation, Constrained Problem, Control Parameters, Differential Evolution, Differential Evolution Algorithms, Ensemble, Evolution Process, Mutation Strategy, Numerical Optimizations, Optimal Performance, Parameter Adaptation, Parameter Adaptive, Parameter Setting, Parameter-tuning, Differentiation (calculus), Global Optimization, Lakes, Optimization, Evolutionary AlgorithmsConstrained problem, Control parameters, Differential Evolution, Differential evolution algorithms, Ensemble, Evolution process, Mutation strategy, Numerical optimizations, Optimal performance, Parameter adaptation, Parameter adaptive, Parameter setting, Parameter-tuning, Differentiation (calculus), Global optimization, Lakes, Optimization, Evolutionary algorithmsDifferential EvolutionParameter AdaptationMutation Strategy AdaptationEnsembleGlobal OptimizationConference Object