Several real-world problems are modeled as multi-objective sequential decision-making with multiple competing objectives, and reinforcement learning (MORL) has garnered attention a solution to this problem. One of the challenges in obtaining desired policy using MORL is that priorities (hereafter, weights) for each objective must be designed advance scalarize reward vector. Determining weights ...

Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding method, but two issues need to be solved extend it the multi-objective field, one how update Pareto archive, other choose elite ant lions from archive. We develop a novel variant of this paper. A new measure combining do...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

supplier selection is one of the most important activities of purchasing departments. this importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services which affect the outcome in the buyer’s company. supplier selection is a multiple criteria decision making problem in which the objectives are not equall...

Abstract For the goal of automated design high-performance deep convolutional neural networks (CNNs), architecture search (NAS) methodology is becoming increasingly important for both academia and industries. Due to costly stochastic gradient descent training CNNs performance evaluation, most existing NAS methods are computationally expensive real-world deployments. To address this issue, we fi...