Energy-Aware Metaheuristics

2026-02-06

AI Summary

This work proposes an adaptive metaheuristic optimization framework that maximizes fitness gains under a fixed energy budget by leveraging operator-level energy efficiency. Introducing the Expected Improvement per Joule (EI/J) metric, the framework dynamically schedules lightweight and heavyweight operators to balance exploration and exploitation while optimizing energy utilization. For the first time, energy efficiency is explicitly integrated into the operator selection mechanism within a steady-state evolutionary algorithm that combines genetic operators, particle swarm optimization, and iterative local search. Evaluated on three combinatorial optimization problems—knapsack instances, NK landscapes, and error-correcting code design—the approach significantly reduces energy consumption compared to baseline methods while maintaining comparable solution quality. Empirical results further show that EI/J values converge early, leading to stable and reliable operator selection, thereby demonstrating the strategy’s generalizability across diverse problem domains.

Technology Category

• Search and Optimization: Evolutionary Computation
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

This paper presents a principled framework for designing energy-aware metaheuristics that operate under fixed energy budgets. We introduce a unified operator-level model that quantifies both numerical gain and energy usage, and define a robust Expected Improvement per Joule (EI/J) score that guides adaptive selection among operator variants during the search. The resulting energy-aware solvers dynamically choose between operators to self-control exploration and exploitation, aiming to maximize fitness gain under limited energy. We instantiate this framework with three representative metaheuristics - steady-state GA, PSO, and ILS - each equipped with both lightweight and heavy operator variants. Experiments on three heterogeneous combinatorial problems (Knapsack, NK-landscapes, and Error-Correcting Codes) show that the energy-aware variants consistently reach comparable fitness while requiring substantially less energy than their non-energy-aware baselines. EI/J values stabilize early and yield clear operator-selection patterns, with each solver reliably self-identifying the most improvement-per-Joule - efficient operator across problems.

Problem

• energy-aware
• metaheuristics
• energy budget
• combinatorial optimization
• operator selection

Innovation

• energy-aware metaheuristics
• Expected Improvement per Joule
• operator-level energy modeling
• adaptive operator selection
• energy-constrained optimization