Unsupervised Fuzzy Multi-Objective Optimization for Complex Decision-Making Problems

Abstract

Many real-world optimization problems involve multiple competing objectives, where the exact weighting is often difficult to define. Traditional fuzzy logic approaches for decision-making, while useful for handling uncertainty, require evaluating all combinations to find the optimal solution. For instance, in logistical scenarios where the optimal location must be determined, fuzzy logic would require evaluating every point on the map, which is time-consuming and computationally expensive. This paper presents an unsupervised neuro-fuzzy optimization model that addresses these problems. Our approach combines a multimodal neural network with differentiable fuzzy objectives for efficient optimization. The model provides robust solutions across various applications, including location selection, network infrastructure, and energy distribution. By leveraging differentiable fuzzy objectives, it can handle complex multi-objective tasks while ensuring high performance and scalability. This method significantly improves traditional optimization techniques and is highly applicable in dynamic, real-time environments.