Energy-Aware Metaheuristics
This work addresses energy efficiency in metaheuristics for combinatorial optimization, offering incremental improvements by adapting existing methods to energy constraints.
The paper tackled the problem of designing metaheuristics that operate under fixed energy budgets by introducing a framework with an Expected Improvement per Joule (EI/J) score for adaptive operator selection, resulting in energy-aware variants that achieved comparable fitness with substantially less energy across three combinatorial problems.
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.