Green Optimization: Energy-aware Design of Metaheuristics by Using Machine Learning Surrogates to Cope with Real Problems

2026-02-06

AI Summary

This work addresses the challenge in real-world optimization where metaheuristic algorithms struggle to balance solution accuracy, computational time, and energy consumption, compounded by a lack of systematic evaluation of how machine learning surrogate models affect energy efficiency. The authors propose an energy-aware surrogate-assisted optimization paradigm that integrates pretrained neural surrogates into metaheuristic frameworks to replace costly objective function evaluations. Through large-scale experiments, they provide the first systematic quantification of the joint impact of static pretraining and dynamic retraining strategies on energy consumption, runtime, memory usage, and solution accuracy. Results demonstrate that the proposed approach reduces energy consumption and execution time by approximately 98% and memory usage by about 99%, while increasing training data further amortizes the per-inference cost—achieving significant gains in energy efficiency without compromising solution quality.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Addressing real-world optimization challenges requires not only advanced metaheuristics but also continuous refinement of their internal mechanisms. This paper explores the integration of machine learning in the form of neural surrogate models into metaheuristics through a recent lens: energy consumption. While surrogates are widely used to reduce the computational cost of expensive objective functions, their combined impact on energy efficiency, algorithmic performance, and solution accuracy remains largely unquantified. We provide a critical investigation into this intersection, aiming to advance the design of energy-aware, surrogate-assisted search algorithms. Our experiments reveal substantial benefits: employing a state-of-the-art pre-trained surrogate can reduce energy consumption by up to 98\%, execution time by approximately 98%, and memory usage by around 99\%. Moreover, increasing the training dataset size further enhances these gains by lowering the per-use computational cost, while static pre-training versus continuous (iterative) retraining have relatively different advantages depending on whether we aim at time/energy or accuracy and general cost across problems, respectively. Surrogates also have a negative impact on costs and accuracy at times, and then they cannot be blindly adopted. These findings support a more holistic approach to surrogate-assisted optimization, integrating energy with time and predictive accuracy into performance assessments.

Problem

• energy-aware optimization
• metaheuristics
• machine learning surrogates
• green computing
• real-world optimization

Innovation

• surrogate-assisted optimization
• energy-aware metaheuristics
• neural surrogates
• green computing
• pre-trained models