一种碳感知蚁群系统（CAACS）
A Carbon Aware Ant Colony System (CAACS)
AI总结：
CAACS算法通过双目标优化解决GTSP问题，兼顾环境与经济目标，适用于网络设计、配送路线规划及商业航空物流等可持续交通场景。
摘要：
在可持续性日益重要的时代，我们介绍了一种新的碳感知蚁群系统（CAACS）算法，该算法在解决广义旅行商问题（GTSP）的同时最小化碳排放。这种新方法利用蚂蚁群落信息素轨迹的自然效率来寻找最优路径，平衡环境和经济目标。通过将可持续性整合到运输模型中，CAACS为现实应用提供了强大的工具，包括网络设计、配送路线规划和商业航空物流。我们的算法独特的双目标优化推进了可持续交通解决方案的研究。
英文摘要：
In an era where sustainability is becoming increasingly crucial, we introduce a new Carbon-Aware Ant Colony System (CAACS) Algorithm that addresses the Generalized Traveling Salesman Problem (GTSP) while minimizing carbon emissions. This novel approach leverages the natural efficiency of ant colony pheromone trails to find optimal routes, balancing both environmental and economic objectives. By integrating sustainability into transportation models, CAACS provides a powerful tool for real-world applications, including network design, delivery route planning, and commercial aircraft logistics. Our algorithm's unique bi-objective optimization advances the study of sustainable transportation solutions.
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。
