Abstract
The Linear Ordering problem (LOP) is an NP-hard problem, which has been solved using different metaheuristic approaches. The best solution for this problem is a memetic algorithm, which uses the traditional approach of hybridizing a genetic algorithm with a single local search; on the contrary, in this paper we present a memetic solution hybridized with multiple local searches through all the memetic process. Experimental results show that using the best combination of local searches, instead of a single local search, the performance for XLOLIB instances is improved by 11.46% in terms of quality of the solution. For the UB-I instances, the proposed algorithm obtained a 0.12% average deviation from the best known solutions, achieving 17 new best known solutions. A Wilcoxon test was performed, ranking the proposed memetic algorithm as the second best solution of the state of the art for LOP. The results show that the multiple local searches approach can be more effective to get a better control in balancing intensification/diversification than the single local search approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Laguna, M., Martí, R., Campos, V.: Intensification and diversification with elite tabu search solutions for the linear ordering problem. Computers and Operations Research 26, 1217–1230 (1998)
Karp, R., Miller, R., Thatcher, J.: Reducibility among Combinatorial Problems. In: Complexity of Computer Computation, pp. 85–103.Plenum Press, New York (1972)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Co, New York (1975)
Leontief, W.W.: Input-Output Economics. Oxford University Press, Oxford (1986)
Schiavinotto, T., Stützle, T.: Search Space Analysis of the Linear Ordering Problem. Darmstadt University of Technology, Intellectics Group, Darmstadt (2004)
Martí, R., Reinelt, G., Duarte, A.: A Benchmark Library and a Comparison of Heuristic Methods for the Linear Ordering Problem. Computational optimization and applications (2010)
Moscato, P., Cota, C.: A Modern Introduction to Memetic Algorithms. In: Gendreau, M., Potvin, J.-Y. (eds.) International Series in Operations Research & Management Science, pp. 449–468. Springer, Heidelberg (2010)
Ong, Y.-S., Keane, A.J.: Meta-Lamarckian Learning in Memetic Algorithms. IEEE Trans. Evol. Comput. 8(2), 99–110 (2004)
Congram Richard, K.: Polynomially Searchable Exponential Neighborhoods for Sequencing Problems in Combinatorial Optimization. Faculty of Mathematical Studies, University of Southampton, UK (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fraire Huacuja, H.J. et al. (2011). Multiple Local Searches to Balance Intensification and Diversification in a Memetic Algorithm for the Linear Ordering Problem. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-21222-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21221-5
Online ISBN: 978-3-642-21222-2
eBook Packages: Computer ScienceComputer Science (R0)