An Empirical Study on Energy Efficiency and Solution Effectiveness of Evolutionary Algorithms
Abstract
The rapid growth of computational intelligence and large-scale optimization has raised concerns regarding the energy consumption and environmental impact of algorithmic processes. Evolutionary Algorithms (EAs), while widely recognized for their robustness and flexibility in solving complex optimization problems, often require extensive computational resources, which directly translate into increased energy usage. This study presents an empirical comparison of several widely used evolutionary algorithms by jointly analyzing their energy consumption and the quality of solutions they produce. Through standardized experimental setups and benchmark optimization problems, the trade-offs between solution optimality and energy efficiency are investigated. Energy metrics are analyzed alongside convergence behavior and final fitness values to provide a holistic assessment of algorithmic performance. The findings highlight that energy-efficient algorithm design and implementation choices can significantly influence sustainability without severely compromising solution quality. This study contributes to the emerging field of energy-aware evolutionary computation by offering experimental evidence and practical insights for researchers and practitioners.
Keywords
Evolutionary Algorithms, Energy Consumption, Optimization, Quality, SolutionHow to Cite
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Copyright (c) 2026 Mihel T Den
This work is licensed under a Creative Commons Attribution 4.0 International License.
Individual articles are published Open Access under the Creative Commons Licence: CC-BY 4.0.
