Abstract
Ant Colony Optimization (ACO) is a stochastic search method that mimic the social behavior of real ants colonies, which manage to establish the shortest rout to feeding sources and back. Such algorithms have been developed to arrive at near-optimal solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. On this paper is proposed an ant algorithm with semi-random start. Several start strategies are prepared at the basis of the start nodes estimation. There are several parameters which manage the starting strategies. In this work we focus on influence on the quality of the achieved solutions of the parameters which shows the percentage of the solutions classified as good and as bad respectively. This new technique is tested on Multiple Knapsack Problem (MKP).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation 1, 53–66 (1997)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press (2004)
Fidanova, S.: Evolutionary Algorithm for Multiple Knapsack Problem. In: Int. Conference Parallel Problems Solving from Nature, Real World Optimization Using Evolutionary Computing, Granada, Spain (2002) ISBN No 0-9543481-0-9
Fidanova, S.: Ant colony optimization and multiple knapsack problem. In: Renard, J.P. (ed.) Handbook of Research on Nature Inspired Computing for Economics ad Management, pp. 498–509. Idea Grup Inc. (2006) ISBN 1-59140-984-5
Fidanova, S., Atanassov, K., Marinov, P., Parvathi, R.: Ant Colony Optimization for Multiple Knapsack Problems with Controlled Starts. Int. J. Bioautomation 13(4), 271–280
Fidanova, S., Marinov, P.: Intuitionistic Fuzzy Estimation of the Ant Methodology. J. of Cybernetics and Information Technologies 9(2), 79–88 (2009) ISSN 1311-9702
Fiodanova, S., Marinov, P., Atanassov, K.: Generalized Net Models of the Process of Anmt Colony Optimization with Different Strategies and Intuitionistic Fuzzy Estimations. In: Proc. Jangjeon Math., vol. 13(1), pp. 1–12 (2010) ISSN 1598-7264, Soc.
Fidanova, S., Atanassov, K., Marinov, P.: Start Strategies of ACO Applied on Subset Problems. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) NMA 2010. LNCS, vol. 6046, pp. 248–255. Springer, Heidelberg (2011)
Fidanova, S., Marinov, P., Atanassov, K.: Sensitivity Analysis of ACO Start Strategies for Subset Problems. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) NMA 2010. LNCS, vol. 6046, pp. 256–263. Springer, Heidelberg (2011)
Fidanova, S., Atanassov, K., Marinov, P.: Intuitionistic Fuzzy Estimation of the Ant Colony Optimization Starting Points. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2011. LNCS, vol. 7116, pp. 222–229. Springer, Heidelberg (2012)
Reiman, M., Laumanns, M.: A Hybrid ACO algorithm for the Capacitated Minimum Spanning Tree Problem. In: Proc. of First Int. Workshop on Hybrid Metahuristics, Valencia, Spain, pp. 1–10 (2004)
Stutzle, T., Dorigo, M.: ACO Algorithm for the Traveling Salesman Problem. In: Miettinen, K., Makela, M., Neittaanmaki, P., Periaux, J. (eds.) Evolutionary Algorithms in Engineering and Computer Science, pp. 163–183. Wiley (1999)
Zhang, T., Wang, S., Tian, W., Zhang, Y.: ACO-VRPTWRV: A New Algorithm for the Vehicle Routing Problems with Time Windows and Re-used Vehicles based on Ant Colony Optimization. In: Sixth International Conference on Intelligent Systems Design and Applications, pp. 390–395. IEEE Press (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fidanova, S., Marinov, P. (2013). Ant Colony Optimization Start Strategies Performance According Some of the Parameters. In: Dimov, I., Faragó, I., Vulkov, L. (eds) Numerical Analysis and Its Applications. NAA 2012. Lecture Notes in Computer Science, vol 8236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41515-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-41515-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41514-2
Online ISBN: 978-3-642-41515-9
eBook Packages: Computer ScienceComputer Science (R0)