Abstract
We suggest a novel memory-based metaheuristic optimization algorithm, VLR, which uses a list of already-visited areas to more effectively search for an optimal solution. We chose the Max-cut problem to test its optimization performance, comparing it with state-of-the-art methods.VLRdominates the previous best-performing heuristics.We also undertake preliminary analysis of the algorithm’s parameter space, noting that a larger memory improves performance. VLR was designed as a general-purpose optimization algorithm, so its performance on other problems will be investigated in future.
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
Chen, K., Rajewsky, N.: The evolution of gene regulation by transcription factors and microRNAs. Nature Reviews Genetics 8(2), 93–103 (2007)
Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)
Chang, K.C., Du, D.: Efficient algorithms for layer assignment problem. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 6(1), 67–78 (1987)
Pinter, R.Y.: Optimal layer assignment for interconnect. Adv. VLSI Comput. Syst. 1(2), 123–137 (1984)
Barahona, F., Grötschel, M., Jünger, M., Reinelt, G.: An application of combinatorial optimization to statistical physics and circuit layout design. Operations Research 36(3), 493–513 (1988)
Karp, R.M.: Reducibility among combinatorial problems. In: Jünger, M., Liebling, T.M., Naddef, D., Nemhauser, G.L., Pulleyblank, W.R., Reinelt, G., Rinaldi, G., Wolsey, L.A. (eds.) 50 Years of Integer Programming 1958-2008, pp. 219–241. Springer, Heidelberg (2010)
Burer, S., Monteiro, R.D.C., Zhang, Y.: Rank-two relaxation heuristics for max-cut and other binary quadratic programs. SIAM Journal on Optimization 12, 503–521 (2000)
Festa, P., Pardalos, P., Resende, M., Ribeiro, C.: Randomized heuristics for the max-cut problem. Optimization Methods and Software 17(6), 1033–1058 (2002)
Martí, R., Duarte, A., Laguna, M.: Advanced scatter search for the max-cut problem. INFORMS J. on Computing 21(1), 26–38 (2009)
Kochenberger, G.A., Hao, J.K., Lü, Z., Wang, H., Glover, F.: Solving large scale max cut problems via tabu search. Journal of Heuristics 19(4), 565–571 (2013)
Glover, F., Lü, Z., Hao, J.K.: Diversification-driven tabu search for unconstrained binary quadratic problems. 4OR, Q. J. Oper. Res. 8(3), 239–253 (2010)
Song, B., Li, V.: A hybridization between memetic algorithm and semidefinite relaxation for the max-cut problem. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, GECCO 2012, pp. 425–432. ACM, New York (2012)
Goemans, M.X., Williamson, D.P.: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42(6), 1115–1145 (1995)
Helmberg, C., Rendl, F.: A spectral bundle method for semidefinite programming. SIAM Journal on Optimization 10, 673–696 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yun, H., Ha, M.H., McKay, R.I. (2014). VLR: A Memory-Based Optimization Heuristic. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-10762-2_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10761-5
Online ISBN: 978-3-319-10762-2
eBook Packages: Computer ScienceComputer Science (R0)