节能元启发式算法
Energy-Aware Metaheuristics
AI总结：
本文提出了一种设计节能元启发式算法的框架，通过统一的算子级模型量化数值收益与能耗，并定义了稳健的每焦耳预期改进(EI/J)评分，指导适应性算子选择。实验表明，节能变体在较少能耗下达到相似适应度。
摘要：
本文提出了一种设计在固定能耗预算下运行的节能元启发式算法的系统框架。我们引入了一个统一的算子级模型，量化了数值收益和能耗，并定义了一个稳健的每焦耳预期改进(EI/J)评分，用于指导搜索过程中的适应性算子选择。所得到的节能求解器能够动态选择算子以自主控制探索与利用，旨在在有限能耗下最大化适应度收益。我们用三种代表性元启发式算法——稳态GA、PSO和ILS——实例化该框架，每种算法均配备轻量级和重型算子变体。在三个异构组合优化问题（背包问题、NK景观和纠错码）上的实验表明，节能变体在较少能耗下达到与非节能基线相当的适应度。EI/J值在早期稳定，并产生清晰的算子选择模式，每个求解器都能可靠地自主识别出在问题中最具每焦耳改进的高效算子。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
