Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 418-424· 0 citations· 28 references
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.
An objective-wise variable analysis method that first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables is proposed.
Chuanlong Ye, Fazhi He, Xiaoxin Gao et al.· Journal of King Saud Univers...· 0 citations
In many real-life problems, decision-making gets complicated due to dual sources of uncertainty, known as randomness and fuzziness or imprecision, which can be challenging for traditional optimization methods. Most existing fuzzy optimization techniques that optimize fuzzy-valued objective functions account for fuzzine...
The results indicate that the integration of fuzzy DEMATEL with fuzzy assignment with restrictions modeling yields a systematic and dependable decision-support framework for addressing intricate assignment challenges in uncertain environments.
V. Vaishalini, G. Uthra· International Journal of Mat...· 0 citations
Multi-objective Bayesian optimization (MOBO) is effective in identifying the Pareto fronts for expensive black-box problems. However, most current MOBO approaches are limited to low-dimensional decision space due to its exponential sampling complexity. This paper presents decision variable interaction analysis-based MO...
Results confirm that embedding hyper-heuristic mechanisms within expert systems provides a powerful approach for addressing complex optimization problems, paving the way for scalable and data-driven intelligent decision-support systems.
A dynamic Bayesian Gaussian mixture model based decision variable grouping evolutionary algorithm that consistently outperforms several state-of-the-art large-scale optimization algorithms with respect to convergence speed, optimization accuracy, and scalability.
