Publication Date: 2023/04/14
Abstract: A neural network for solving fuzzy multiple objective linear programming problems is proposed in this paper. The distinguishing features of the proposed Neural network are that the primal and dual problems can be solved simultaneously, all necessary and sufficient optimality conditions are incorporated, and no penalty parameter is involved. we prove strictly an important theoretical result so that, for an arbitrary initial point, the trajectory of the proposed network does converge to the set of its equilibrium points, regardless of whether a multiple objective linear programming problem has unique or infinitely many optimal solutions. Numerical simulation results also show that the proposed network is feasible and efficient. In addition, a general method for transforming nonlinear programming problems into unconstrained problems is also proposed.
Keywords: Fuzzy Neural Network ,Fuzzy Multiple objective ,Neuro-fuzzy, Constraint satisfaction Learning Fuzzy constraints, Linear Programming Problems .
DOI: https://doi.org/10.5281/zenodo.7827681
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23APR034.pdf
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