Abstract
In this chapter we propose a new class of cellular algorithms. There exists a variety of cellular algorithm approaches but most of them do not structure the search process. In this work we propose a cellular processing approach to solve optimization problems. The main components of these algorithms are: the processing cells (PCells), the communication between PCells, and the global and local stagnation detection. The great flexibility and simplicity of this approach permits pseudo-parallelization of one or several different metaheuristics. To validate our approach, the linear ordering problem with cumulative costs (LOPCC) was used to describe two cellular processing algorithms, whose performance was tested with standard instances. The experimental results show that the cellular processing algorithms increase solution quality up to 3.6% and reduce time consumption up to 20% versus the monolithic approach. Also the performance of these algorithms is statistically similar to those of the state-of-the-art solutions, and they were able to find 38 new best-known solutions (i.e., not previously found by other algorithms) for the instances used. Finally, it is important to point out that these encouraging results indicate that the field of cellular processing algorithms is a new and rich research area.
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.
References
Alba, E., Dorronsoro, B., Alfonso, H.: Cellular memetic algorithms. Journal of Computer Science and Technology 5(4), 257–263 (2005)
Benvenuto, N., Carnevale, G., Tomasin, S.: Optimum power control and ordering in SIC receivers for uplink CDMA systems. In: IEEE-ICC 2005 (2005)
Bertacco, L., Brunetta, L., Fischetti, M.: The linear ordering problem with cumulative costs. Eur. J. Oper. Res. 189(3), 1345–1357 (2008)
Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997), doi:10.1109/4235.585892
Duarte, A., Laguna, M., Marti, R.: Tabu search for the linear ordering problem with cumulative costs. Computational Optimization and Applications 48, 697–715 (2011)
Duarte, A., Marti, R., Alvarez, A., Angel Bello, F.: Metaheuristics for the linear ordering problem with cumulative costs. European Journal of Operational Research 216(2), 270–277 (2012)
Feo, T., Resende, M.: A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8(2), 67–71 (1989)
Feo, T., Resende, M.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6(2), 109–133 (1995)
Feo, T., Resende, M., Smith, S.: A greedy randomized adaptive search procedure for maximum independent set. Operations Research 42(5), 860–878 (1994)
Folino, G., Pizzuti, C., Spezzano, G., Spezzano, O.: Combining cellular genetic algorithms and local search for solving satisfiability problems. In: Proceedings of Tenth IEEE International Conference on Tools with Artificial Intelligence, pp. 192–198 (1998)
Glover, F.: Heuristics for integer programming using surrogate constraints. Decis. Sci. 8, 156–166 (1977)
Huy, N.Q., Soon, O.Y., Hiot, L.M., Krasnogor, N.: Adaptive cellular memetic algorithms. Evolutionary Computation 17(2), 231–256 (2009) ISSN:1063-6560
Laguna, M., Marti, R., Campos, V.: Intensification and diversification with elite tabu search solutions for the linear ordering problem. Computers & Operations Research 26(12), 1217–1230 (1999), doi: 10.1016/s0305- 0548(98)00104-x
Li, B., Zhao, X.-F., Z.Q.s.T.S.h: Differentiate coevolutionary algorithms. Journal of Convergence Information Technology 6(4), 3247–3259 (2011)
Prais, M., Ribeiro, C.: Parameter variation in GRASP procedures. Investigacion Operativa 9, 1–20 (2000)
Reinelt, G.: The linear ordering problem: Algorithms and applications. Mathematical Social Sciences 14(2), 199–200 (1985)
Resende, M., Riberio, C.: Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications. In: Handbook of Metaheurictics, vol. 146, pp. 283–319. Springer (2010)
Righini, G.: A branch-and-bound algorithm for the linear ordering problem with cumulative costs. European Journal of Operational Research 186, 965–971 (2008)
Seredynski, F., Zomaya, A., Bouvry, P.: Function optimization with coevolutionary algorithms. In: International Intelligent Information Processing and Web Mining Conference, Zakopane, Poland (June 2003)
Sipper, M.: The emergence of cellular computing. IEEE Computer 32(7), 18–26 (1999)
Teran-Villanueva, J., Fraire-Huacuja, H., Duarte, A., Pazos-Rangel, R., Carpio Valadez, J., Puga-Soberanes, H.: Improving Iterated Local Search Solution for the Linear Ordering Problem with Cumulative Costs (LOPCC). In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010, Part II. LNCS, vol. 6277, pp. 183–192. Springer, Heidelberg (2010)
Teran-Villanueva, J., Pazos-Rangel, R., Martinez, J.A., Lopez-Loces, M.C., Zamarron-Escobar, D., Pineda, A.: Hybrid GRASP with composite local search and path-relinking for the linear ordering problem with cumulative costs. International Journal of Combinatorial Optimization Problems and Informatics 3(1), 21–30 (2012)
Wiegand, R.P.: An analysis of cooperative coevolutionary algorithms. Ph.D. thesis, Fairfax, VA, USA (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Terán-Villanueva, J.D., Huacuja, H.J.F., Valadez, J.M.C., Pazos Rangel, R.A., Soberanes, H.J.P., Flores, J.A.M. (2013). Cellular Processing Algorithms. In: Melin, P., Castillo, O. (eds) Soft Computing Applications in Optimization, Control, and Recognition. Studies in Fuzziness and Soft Computing, vol 294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35323-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-35323-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35322-2
Online ISBN: 978-3-642-35323-9
eBook Packages: EngineeringEngineering (R0)