Constraint reformulations for set point optimization problems using fuzzy cognitive map models

Abstract

The selection of optimal set points is an important problem in modern process control. Fuzzy cognitive maps (FCMs) allow to construct models of complex processes using expert knowledge, which is particularly useful in situations where measuring the variables of interest online is problematic. These models can be used as constraints in optimization problems with the objective of determining optimal set points for those processes. This article presents a reformulation of the constraints imposed by the FCM models that reduces the complexity of the resulting optimization problem and enables the application of heuristic methods for its solution. Computational results show that the use of separable programming on the reformulated problem constitutes a very good alternative, both in terms of solution time and reliability in finding the optimum, enabling the application of FCM modeling to larger systems and easing the practical implementation of the approach.

Exploration of Solving Methods and Applications of Neural Network Models in Optimization Problems

“…In recent years, the rapid growth of AI technology has attracted widespread attention worldwide [1]. From smart homes to autonomous driving, from medical diagnosis to financial analysis, the application of AI is ubiquitous, and it is gradually penetrating into every aspect of our lives, changing our work and lifestyle [2].…”