A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

The dynamic traveling salesman problem (DTSP) is significant in logistics distribution real-world applications smart cities, but it uncertain and difficult to solve. This paper proposes a scheme library-based ant colony optimization (ACO) with two-optimization (2-opt) strategy solve the DTSP efficiently. work novel contributes three aspects: model, framework, algorithm design. Firstly, traditional models often consider change of travel distance between two nodes over time, while this focuses on special model that node locations dynamically time. Secondly, ACO carried out an offline online application framework efficiently reuse historical information help fast respond environment. proposed due fact environmental caused by location, therefore new environment somehow similar certain previous environments. way, optimization, solutions for possible changes are optimized advance, stored mode library. In application, when detected, candidate library reused via improve search efficiency reduce computational complexity. Thirdly, design, cooperates 2-opt enhance efficiency. To evaluate performance 2-opt, we design challenging cases up 200 1379 compare them other genetic algorithms. experimental results show can DTSPs effectively.