在中国衡阳市,优化城市固体废物 (MSW) 收集路线,使用基于Amap应用程序编程接口 (API) 的增强基因算法
Siyu Zhang1, Xiaowen Zhang1, Yanan Xiao1
1School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China.
Waste management (New York, N.Y.)
|March 3, 2026
概括
改进的遗传算法优化了废物收集路线,与其他方法相比,总距离减少了31%,成本显著降低. 这提高了城市卫生管理的效率,降低了城市卫生管理对环境的影响.
科学领域:
- 环境科学 环境科学
- 运营研究 运营研究
- 计算机科学 计算机科学
背景情况:
- 人口增长和城市化需要高效的,低碳的废物收集环境卫生.
- 传统的遗传算法 (GA) 在复杂的现实垃圾收集路由中面临着缓慢的融合和计算瓶等挑战.
- 诸如空运行和绕道等问题困扰着当前的废物收集系统.
研究的目的:
- 为废物收集开发一个车辆路线优化模型,尽量减少运输,固定和碳排放成本.
- 解决高密度城市环境中传统算法的局限性.
- 为了提高废物收集路线优化的效率和现实世界的适用性.
主要方法:
- 开发了一个车辆路由优化模型,通过Amap API将真实道路网络数据纳入其中.
- 提出了一种改进的遗传算法 (EGA),具有适应性参数调整,组合突变和精英保存策略.
- 综合人口多样性反以改善全球搜索,融合速度和计算效率.
主要成果:
- 增强基因算法 (EGA) 展示了卓越的全球搜索能力,融合速度,稳定性和计算效率.
- 与标准的遗传算法 (GA) 相比,优化路线实现了总距离减少31%.
- 实现了显著的成本节约:55%与模拟化 (SA) 相比,90%与粒子群优化 (PSO) 相比,以及12%与殖民地优化 (ACO) 相比.
结论:
- 拟议的增强基因算法 (EGA) 为垃圾收集车辆路线优化提供了一个高度有效的解决方案.
- 该模型为现实世界城市卫生挑战提供了可行和高效的解决方案,降低了运营成本和环境影响.
- 在距离缩短和成本效益方面,EGA显著超过传统算法.
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