通过算法复杂性与群体智能优化ICT中的碳足迹
Optimizing Carbon Footprint in ICT through Swarm Intelligence with Algorithmic Complexity
AI总结：
本研究针对ICT行业高能耗导致的碳排放问题，提出利用群体智能优化碳足迹评估，通过构建凸优化问题量化不同算法的资源强度与环境影响。
摘要：
2022年记录了化石燃料燃烧和水泥生产的全球排放量，标志着排放量恢复至疫情前水平，并明确表明排放峰值尚未实现。信息与通信技术（ICT）行业因其巨大的能源消耗，对这一上升趋势做出了重大贡献。这表明需要进一步探索群体智能应用，以测量和优化ICT内的碳足迹。所有致动因素均基于数据收集质量进行评估；量化了各来源的差异；并优化了与ICT能源管理中碳足迹相关的目标函数。重点放在数据源的异步整合上，以构建凸优化问题。解决了防止碳足迹评估准确性受损的必然需求。复杂性百分比从Bat Algorithm的5.25%到Fast Bacterial Swarming的7.87%不等，表明算法间资源强度存在显著波动。这些发现表明，我们能够量化各种群体算法的环境影响。
英文摘要：
Global emissions from fossil fuel combustion and cement production were recorded in 2022, signaling a resurgence to pre-pandemic levels and providing an apodictic indication that emission peaks have not yet been achieved. Significant contributions to this upward trend are made by the Information and Communication Technology (ICT) industry due to its substantial energy consumption. This shows the need for further exploration of swarm intelligence applications to measure and optimize the carbon footprint within ICT. All causative factors are evaluated based on the quality of data collection; variations from each source are quantified; and an objective function related to carbon footprint in ICT energy management is optimized. Emphasis is placed on the asyndetic integration of data sources to construct a convex optimization problem. An apodictic necessity to prevent the erosion of accuracy in carbon footprint assessments is addressed. Complexity percentages ranged from 5.25% for the Bat Algorithm to 7.87% for Fast Bacterial Swarming, indicating significant fluctuations in resource intensity among algorithms. These findings suggest that we were able to quantify the environmental impact of various swarm algorithms.
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。
