Optimization methodology based on neural networks and reference point algorithm applied to fuzzy multiobjective optimization problems

Artificial neural networks are massively paralleled distributed computation and fast convergence and can be considered as an efficient method to solve real-time optimization problems. In this paper, we propose reference point based neural network algorithm for solving fuzzy multiobjective optimization problems MOOP. The target is to identify the Pareto-optimal region closest to the reference points. Our approach has two characteristic features. Firstly, fuzzy multiobjective optimization problem (F-MOOP) has been transformed to crisp multiobjective optimization problem (C-MOOP) by means of Alpha-cut. Secondly a neural networks based reference point algorithm is implemented to solve C-MOOP in such a way that they integrate the decision maker DM early in the optimization process instead of leaving him/her alone with the final choice of one solution among the whole Pareto optimal set. Such procedures will provide the DM with a set of solutions near her/his preference so that a better and a more reliable decision can be made. Simulation runs on engineering application problems demonstrate their usefulness in practice and show another use of a neural network methodology in allowing the DM to solve multiobjective optimization problems better and with more confidence.