Abstract
Traveling Salesman Problem is a well-known NP-Hard problem, which aims at finding the shortest path between numbers of cities. Chemical Reaction Optimization (CRO) is a recently established meta-heuristic algorithm for solving optimization problems which has successfully solved many optimization problems. The main goal of this paper is to investigate the possibility of parallelizing CRO for solving the TSP problem called (PCRO). PCRO is compared with Genetic Algorithm (GA), which is a well-known meta-heuristic algorithm. Experimental results show relatively better performance for PCRO in terms of execution time, Speedup, optimal cost and Error rate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vukmirović, S., Pupavac, D.: The Travelling Salesman Problem in the Function of Transport Network Optimalization. Fakulty of Economics, Interdisciplinary Management Research IX, University in Osijek, Osijek (2013)
Zhan, F., Noon, C.: Shortest path algorithms: an evaluation using real road networks. Transp. Sci. (1996)
Al-Shaikh, A., Khattab, H., Sharieh, A., Sleit, A.: Resource utilization in cloud computing as an optimization problem. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(6), 336–342 (2016)
Hoffman, K.L., Padberg, M., Rinaldi, G.: Traveling salesman problem. In: Encyclopedia of Operations Research and Management Science, pp 1573–1578. Springer (2016)
Lam, A.Y.S., Li, V.O.K.: Chemical reaction optimization: a tutorial. Memet. Comput. 4, 3–17 (2012)
Barney, B.: Introduction to Parallel Computing. Lawrence Livermore National Laboratory (2007). https://computing.llnl.gov/tutorials/parallel_comp/
Sleit, A., Salah, I., Jabay, R.: Approximating images using minimum bounding rectangles. In: ICADIWT 2008, pp. 394–396 (2008) https://doi.org/10.1109/ICADIWT.2008.4664379
Ostrouchov, G.: Parallel computing on a hypercube: an overview of the architecture and some applications. In: Heiberger, R.M. (ed.) Proceedings of the 19th Symposium on the Interface of Computer Science and Statistics, pp. 27–32. American Statistical Association (1987)
Kiasari, A., Sarbazi-Azad, H.: Analytic performance comparison of hypercubes and star graphs with implementation constraints. J. Comput. Syst. Sci. 74(6), 1000–1012 (2008)
Cathleen, L.: “Inside a NASA Production Supercomputing Center” Concept To Reality magazines, Summer/Fall issue (2011)
Mohan, A., Remya, G.: A parallel implementation of ant colony optimization for TSP based on MapReduce framework. Int. J. Comput. Appl. 88(8), 9–12 (2014)
Er, H.R., Erdogan, N.: Parallel genetic algorithm to solve traveling salesman problem on MapReduce framework using Hadoop cluster”. arXiv preprint arXiv:1401.6267 (2014)
Sun, J., Wang, Y., Li, J., Gao, K.: Hybrid algorithm based on chemical reaction optimization and Lin-Kernighan local search for the traveling salesman problem (2011)
Shaheen, A., Sleit, A.: Comparing between different approaches to solve the 0/1 Knapsack problem. Int. J. Comput. Sci. Netw. Secur. 16(7), 1–10 (2016)
Barham, R., Sharieh, A., Sliet, A.: Chemical reaction optimization for max flow problem. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 7(8), 189–196 (2016)
Deb, K.: An introduction to genetic algorithms. Sadhana 24(4–5), 293–315 (1999)
TSP Website: A collection of worldwide benchmark datasets (2009). http://www.math.uwaterloo.ca/tsp/world/countries.html. Accessed 15 Dec 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Shaheen, A., Sleit, A., Al-Sharaeh, S. (2019). Chemical Reaction Optimization for Traveling Salesman Problem Over a Hypercube Interconnection Network. In: Silhavy, R. (eds) Cybernetics and Algorithms in Intelligent Systems . CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-91192-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-91192-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91191-5
Online ISBN: 978-3-319-91192-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)