Paper Title
Multi-objective Optimization Integrating Artificial Neural Networks: Design and Performance Analysis of a Solar-Powered System for Simultaneous Production of Hydrogen, Electricity, and Heat

Abstract
This study presents a novel approach integrating multi-objective optimization techniques with Artificial Neural Networks (ANNs) to design and analyze the performance of a solar-powered system aimed at simultaneously generating hydrogen, electricity, and heat. The proposed methodology leverages the capability of ANNs to model complex relationships within the system and employs Grey-wolf optimization to achieve optimal operation. Results demonstrate the effectiveness of the integrated approach, with the ANN achieving high accuracy (0.99997) and the TOPSIS was utilized to select the optimal solution. Moreover, sensitivity analyses highlight the significance of all parameters in influencing system behavior. Keywords - ANN; Multi-Objective Optimization; Grey-Wolf; Solar Energy; Hydrogen Production