Clonal Selection Algorithm for Solving Permutation Optimisation Problems: A Case Study of Travelling Salesman Problem

DOI

10.2991/lemcs-15.2015.110How to use a DOI?

Keywords

Clonal Selection Algorithm; Artificial Immune System; Travelling Salesman Problem; Optimisation; Local Search

Abstract

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.

Copyright

© 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/).

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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  -