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Neural Algorithms for Solving Some Multi Criterion Optimization Problems


Jerzy Balicki


Vol. 7  No. 6  pp. 193-201


In this paper, artificial neural networks for solving multiobjective optimization problems have been considered. The Tank-Hopfield model for linear programming has been extended, and then the neural model for finding Pareto-optimal solutions in the linear multi-criterion optimization problem with continuous decision variables has been discussed. Furthermore, the model for solving quasi-quadratic multiobjective optimization problems has been studied. What is more, some models of the Hopfield neural network for solving NP-hard combinatorial multi-criterion optimization problems have been proposed. Finally, the family of extended Hopfield models for finding Pareto-optimal solutions has been developed.


Neural networks, efficient solutions, multi-criterion optimization