MOEA/D with adaptive operator selection for the environmental/economic dispatch problem

Abstract

Adaptive Operator Selection (AOS) is a method used to dynamically determine which operator should be applied in an optimization algorithm based on its performance history. Upper Confidence Bound (UCB) algorithms have been successfully applied for this task due to its ability to tackle the Exploration versus Exploitation (EvE) dilemma presented on AOS… (More)

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