Abstract
Finding shortest path considering multiple objectives is a widely studied graph problem which yields multiple optimal paths called the pareto optimal path set by applying dominance principle. Literature reveals that solving such problems within polynomial time is difficult even for smaller instances using traditional algorithms. This study investigates the convergence of swarm intelligent algorithms and compares them with performance metrics such as the pareto coverage, divergence rate, convergence time and cost for different sample networks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Tarapata, Z.: Selected multicriteria shortest path problems: an analysis of complexity, models and adaptation of standard algorithms. Int. J. Appl. Math. Comput. Sci. 17, 269–287 (2007). https://doi.org/10.2478/v10006-007-0023-2
Haddad, O.B., Mirmomeni, M., Mehrizi, M.Z., Marino, M.A.: Finding the shortest path with honey-bee mating optimization algorithm in project management problems with constrained/unconstrained resources. Comput. Optim. Appl. 47, 97–128 (2008). https://doi.org/10.1007/s10589-008-9210-9
Tarapata, Z.: Military route planning in battlefield simulation: effectiveness problems and potential solutions. J. Telecommun. Inf. Technol. 4, 47–56 (2003)
Hui, L., Yonghui, C.: Study of heuristic search and exhaustive search in search algorithms of the structural learning. In: IEEE Second International Conference on Multimedia and Information Technology, pp. 169–171. IEEE (2010). https://doi.org/10.1109/mmit.2010.163
Häckel, S., Fischer, M., Teich, T., Zechel, D.: A multi-objective ant colony approach for pareto-optimization using dynamic programming categories and subject descriptors. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 33–40 (2008)
Liu, L., Mu, H., Luo, H., Li, X.: A simulated annealing for multi-criteria network path problems. Comput. Oper. Res. 39, 3119–3135 (2012). https://doi.org/10.1016/j.cor.2012.03.013
Mohanta, K.: Comprehensive study on computational methods for k-shortest paths problem. Int. J. Comput. Appl. 40, 22–26 (2012)
Reinhardt, L.B., Pisinger, D.: Multi-objective and multi-constrained non-additive shortest path problems. Comput. Oper. Res. 38, 605–616 (2011). https://doi.org/10.1016/j.cor.2010.08.003
Perny, P., Spanjaard, O.: Near admissible algorithms for multiobjective search. In: European Conference on Artificial Intelligence (ECAI), pp. 490–494 (2008)
Aljazzar, H., Leue, S.: K*: a heuristic search algorithm for finding the k shortest paths. Artif. Intell. 175, 2129–2154 (2011). https://doi.org/10.1016/j.artint.2011.07.003
Beg, S., Khan, A., Nauman, U., Mohsin, S.: Performance evaluation of bionomic algorithm (BA) in comparison with genetic algorithm (GA) for shortest path finding problem. Int. J. Comput. Sci. Issues 8, 238–242 (2011)
Demeyer, S., Goedgebeur, J., Audenaert, P., Pickavet, M., Demeester, P.: Speeding up Martins’ algorithm for multiple objective shortest path problems. 4OR 11, 323–348 (2013). https://doi.org/10.1007/s10288-013-0232-5
Monteiro, M.S.R., Fontes, D.B.M.M., Fontes, F.A.C.C.: An ant colony optimization algorithm to solve the minimum cost network flow problem with concave cost functions. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 139–145. ACM Press, New York. https://doi.org/10.1145/2001576.2001596
Yoshikawa, M., Otani, K.: Ant colony optimization routing algorithm with Tabu search. In: Proceedings International Multi Conference Engineers Computer Science, vol. III, pp. 17–20 (2010)
Yousefikhoshbakht, M., Didehvar, F., Rahmati, F.: Modification of the ant colony optimization for solving the multiple traveling salesman problem. Rom. J. Inf. Sci. Technol. 16, 65–80 (2013)
Naqvi, N.Z., Matheru, H.K., Chadha, K.: Review of ant colony optimization algorithms onvehicle routing problems and introduction toestimation-based ACO. In: Proceedings of International Conference on Environment Science and Engineering, vol. 8, pp. 161–166 (2011). http://cpfd.cnki.com.cn/Article/CPFDTOTAL-CDYA201104003036.htm
Tang, J., Guan, J., Yu, Y., Chen, J.: Beam search combined with MAX-MIN ant systems and benchmarking data tests for weighted vehicle routing problem. IEEE Trans. Autom. Sci. Eng. 1–13 (2013). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6705646
Girsang, A.S., Tsai, C.-W., Yang, C.-S.: A fast bee colony optimization for traveling salesman problem. In: 2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications. IEEE. pp. 7–12 (2012). https://doi.org/10.1109/ibica.2012.44
Nikolić, M., Teodorović, D.: Transit network design by bee colony optimization. Expert Syst. Appl. 40, 5945–5955 (2013). https://doi.org/10.1016/j.eswa.2013.05.002
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Devarajan, J.P., Paul Robert, T. (2018). Swarm Intelligent Approaches for Solving Shortest Path Problems with Multiple Objectives. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_14
Download citation
DOI: https://doi.org/10.1007/978-981-10-9059-2_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9058-5
Online ISBN: 978-981-10-9059-2
eBook Packages: Computer ScienceComputer Science (R0)