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Utilizing a projection neural network to convex quadratic multi-objective programming problems
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DOI：10.1002/acs.3603.png)
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Abstract
En 中文
In this paper, we propose a projection neural network model for solving convex quadratic multi-objective optimization problem (CQMOP). The CQMOP is first converted into an equivalent convex nonlinear programming problem by the means of the weighted sum method, where the Pareto optimal solutions are calculated via different values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of the suggested model. Both theoretical and numerical approaches are studied. Numerical results are in good agreement with the proved theoretical concepts.
Keywords:
convergence
convex non-linear programming problem
multi-objective optimization problem
neural networks
Pareto optimal solution
stability
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