A Sustainable Multi-Objective Framework for Green Neural Architecture Optimization Using Grey Wolf Optimizer

Abstract

The rising computational demands of deep learning models have intensified concerns regarding their energy consumption and environmental impact, motivating the development of Green Artificial Intelligence (Green AI) approaches. This paper proposes a multi-objective Green AI optimization framework based on the Grey Wolf Optimizer (GWO) to design efficient multilayer perceptron (MLP) architectures. Unlike conventional strategies that focus solely on maximizing accuracy, the proposed method jointly optimizes validation accuracy, training time, number of trainable parameters, and estimated floating-point operations (FLOPs). Evaluated on the Fashion-MNIST dataset and compared against a baseline MLP and Random Search, the GWO-based approach achieves competitive predictive performance while drastically reducing model size, computational complexity, and training time. Pareto front analysis confirms that GWO consistently identifies non-dominated architectures that offer superior trade-offs between accuracy and efficiency. Additional equal-accuracy evaluations demonstrate improved convergence efficiency and stability despite reduced model complexity. The results provide empirical evidence, within the MLP design setting considered in this study, that bio-inspired multi-objective optimization can support Green AI by identifying more compact and efficient architectures with competitive predictive performance.