Clonal Selection Algorithm for Solving Permutation Optimisation Problems: A Case Study of Travelling Salesman Problem
- 10.2991/lemcs-15.2015.110How to use a DOI?
- Clonal Selection Algorithm; Artificial Immune System; Travelling Salesman Problem; Optimisation; Local Search
As an attempt to solve Permutation Optimisation Problems (POP) by using CLONALG (Clonal Selection Algorithm), a well-established artificial immune system, Traveling Salesman Problem (TSP) is studied as an example in this paper. Operators of CLONALG, especially the hyper-mutation operators are analyzed and modified to make CLONALG adapt to POP. Furthermore, the local search technique is employed to speed up the mature of the repertoire system, and receptor-editing operator is also employed to avoid the premature of the antibody population. Finally, several benchmark problems in TSPLIB are tested to evaluate the best and average performance of the proposed algorithm. Experimental results show the proposed competitive algorithm performs better than both the standard CLONALG and a genetic algorithm.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Wei Pang AU - Kangping Wang AU - Yan Wang AU - Ge Ou AU - Hanbing Li AU - Lan Huang PY - 2015/07 DA - 2015/07 TI - Clonal Selection Algorithm for Solving Permutation Optimisation Problems: A Case Study of Travelling Salesman Problem BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 575 EP - 580 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.110 DO - 10.2991/lemcs-15.2015.110 ID - Pang2015/07 ER -