人类多任务处理
备份
计算机科学
趋同(经济学)
任务(项目管理)
布线(电子设计自动化)
车辆路径问题
人口
电池(电)
进化算法
分布式计算
算法
机器学习
嵌入式系统
操作系统
工程类
心理学
功率(物理)
物理
人口学
系统工程
量子力学
社会学
经济
经济增长
认知心理学
作者
Yanguang Cai,Yanlin Wu,Chien Fang
标识
DOI:10.1016/j.eswa.2023.121600
摘要
This study addresses the challenges of the electric vehicle routing problem with time windows, backup batteries, and battery swapping stations (EVRPTW-BB-BSS), reflecting complex issues in modern logistics. We treat each problem as an optimization task. Our main goal is to optimize various types of tasks at the same time. To achieve this, we present the double-assistant evolutionary multitasking algorithm (DAEMTA). This algorithm skillfully combines internal and external knowledge exchange. It helps in the transfer between different and similar types of tasks. DAEMTA includes a two-stage system for collaboration and adaptive methods for managing knowledge transfer. A population enhancement strategy deepens the search for valuable solutions. It also maintains diversity to avoid early convergence. Our experiments show that DAEMTA performs better than advanced EMTAs and traditional algorithms. We test it on 30 multitasking EVRPTW-BB-BSSs and four real-world problems. These findings highlight the practicality and effectiveness of our approach.
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