A Genetic Algorithm with New Local Operators for Multiple Traveling Salesman Problems
- https://doi.org/10.2991/ijcis.11.1.53How to use a DOI?
- Multiple Traveling Salesman Problem, Genetic Algorithm, Branch and Bound algorithm, Local operators
Multiple Traveling Salesman Problem (MTSP) is able to model and solve various real-life applications such as multiple scheduling, multiple vehicle routing and multiple path planning problems, etc. While Traveling Salesman Problem (TSP) focuses on searching a path of minimum traveling distance to visit all cities exactly once by one salesman, the objective of the MTSP is to find m paths for m salesmen with a minimized total cost - the sum of traveling distances of all salesmen through all of the respective cities covered. They have to start from a designated depot which is the departing and returning location of all salesmen. Since the MTSP is a NP-hard problem, a new effective Genetic Algorithm with Local operators (GAL) is proposed in this paper to solve the MTSP and generate high quality solution within a reasonable amount of time for real-life applications. Two new local operators, Branch and Bound (BaB) and Cross Elimination (CE), are designed to speed up the convergence of the search process and improve the solution quality. Results demonstrate that GAL finds a better set of paths with a 9.62% saving on average in cost comparing to two existing MTSP algorithms.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Cite this article
TY - JOUR AU - Kin-Ming Lo AU - Wei-Ying Yi AU - Pak-Kan Wong AU - Kwong-Sak Leung AU - Yee Leung AU - Sui-Tung Mak PY - 2018 DA - 2018/02/05 TI - A Genetic Algorithm with New Local Operators for Multiple Traveling Salesman Problems JO - International Journal of Computational Intelligence Systems SP - 692 EP - 705 VL - 11 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.11.1.53 DO - https://doi.org/10.2991/ijcis.11.1.53 ID - Lo2018 ER -