TY - GEN
T1 - Hybrid Evolutionary Approaches with Tailor-Made Variation Operators for Multi-depot Multiple Traveling Salesman Problem
AU - Chauhan, Sanjeev
AU - Singh, Alok
AU - Mallipeddi, Rammohan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Single depot multiple traveling salesman problem (MTSP) is widely studied in the literature. However, multi-depot multiple traveling salesman problem (MD-MTSP) has not gained much attention. Only a few approaches exist in the literature for MD-MTSP. In this paper, we have proposed two hybrid evolutionary approaches for MD-MTSP. Our first approach is based on grouping genetic algorithm (GGA), whereas the other approach utilizes discrete differential evolution (DDE). Chromosome encoding and variation operators in these two approaches are designed considering the characteristics of MD-MTSP. The solutions obtained through variation operators in these approaches are improved further through a local search. We have compared the performance of our approaches with the best approach available in the literature on standard benchmark instances. In addition, we have reported the performance of our approaches on some large instances also. Computational results show the effectiveness of our two approaches in solving MD-MTSP as these two consistently outperform the existing best approach.
AB - Single depot multiple traveling salesman problem (MTSP) is widely studied in the literature. However, multi-depot multiple traveling salesman problem (MD-MTSP) has not gained much attention. Only a few approaches exist in the literature for MD-MTSP. In this paper, we have proposed two hybrid evolutionary approaches for MD-MTSP. Our first approach is based on grouping genetic algorithm (GGA), whereas the other approach utilizes discrete differential evolution (DDE). Chromosome encoding and variation operators in these two approaches are designed considering the characteristics of MD-MTSP. The solutions obtained through variation operators in these approaches are improved further through a local search. We have compared the performance of our approaches with the best approach available in the literature on standard benchmark instances. In addition, we have reported the performance of our approaches on some large instances also. Computational results show the effectiveness of our two approaches in solving MD-MTSP as these two consistently outperform the existing best approach.
KW - Discrete differential evolution
KW - Grouping genetic algorithm
KW - Intelligent optimization
KW - Multi-depot multiple travelling salesman problem
UR - https://www.scopus.com/pages/publications/105029856959
U2 - 10.1007/978-3-032-16632-6_23
DO - 10.1007/978-3-032-16632-6_23
M3 - Conference contribution
AN - SCOPUS:105029856959
SN - 9783032166319
T3 - Lecture Notes in Computer Science
SP - 358
EP - 376
BT - Distributed Computing and Intelligent Technology - 22nd International Conference, ICDCIT 2026, Proceedings
A2 - Chatterjee, Bapi
A2 - Kothapalli, Kishore
A2 - Mittal, Neeraj
A2 - Natarajan, Arul Murugan
A2 - Singh, Dinesh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2026
Y2 - 16 January 2026 through 19 January 2026
ER -