Abstract
The cyclic antibandwidth problem is to embed the vertices of a graph G of n vertices on a cycle C n such that the minimum distance (measured in the cycle) of adjacent vertices is maximized. Exact results/conjectures for this problem exist in the literature for some standard graphs, such as paths, cycles, two-dimensional meshes, and tori, but no algorithm has been proposed for the general graphs in the literature reviewed by us so far. In this paper, we propose a memetic algorithm for the cyclic antibandwidth problem (MACAB) that can be applied on arbitrary graphs. An important feature of this algorithm is the use of breadth first search generated level structures of a graph to explore a variety of solutions. A novel greedy heuristic is designed which explores these level structures to label the vertices of the graph. The algorithm achieves the exact cyclic antibandwidth of all the standard graphs with known optimal values. Based on our experiments we conjecture the cyclic antibandwidth of three-dimensional meshes, hypercubes, and double stars. Experiments show that results obtained by MACAB are substantially better than those given by genetic algorithm.
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Notes
Refers to amount of time MATLAB takes to complete the operations specified.
References
Bansal R, Srivastava K, Shweta, Varshney K, Sharma N (2008) An evolutionary algorithm for the 2-page crossing number problem. In: Proceedings of IEEE congress on evolutionary computation (CEC2008) pp 1095–1102
Boughaci D, Benhamou B, Drias H (2009) A memetic algorithm for the optimal winner determination problem. Soft Comput 13:905–917
Burke E, Cowling P, Causmaecker DP, Berghe GV (2001) A memetic approach to the nurse rostering problem. App Intell 15(3):199–214
Cappanera P (1999) A survey on obnoxious facility location problems. Technical report TR-99- 11, Dipartimento di Informatica, Uni. di Pisa
Cavicchio DJ (1970) Adaptive search using simulated evolution. PhD thesis, University of Michigan, Ann Arbor
Dawkins R (1976) The selfish gene. Clarendon Press, Oxford
Diaz J, Petit J, Serna M (2002) A survey of graph layout problems. ACM Comput Surv 34:313–356
Eiben AE, Schippers CA (1998) On the evolutionary exploration and exploitation. Fundam Inf 35:35–50
Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York
Franca PM, Mendes A, Moscato P (2001) A memetic algorithm for the total tardiness single machine scheduling problem. Eur J Oper Res 132(1):224–242
Goldberg DE, Deb K (1991) A comparison of selection schemes used in genetic algorithms. In: Foundation of Genetic Algorithms 1 (FOGA-1)
Hart WE, Krasnogor N, Smith JE (eds) (2005) Recent advances in memetic algorithms. In: Series: studies in fuzziness and soft computing, vol 166
Hasan SMK, Sarker R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memetic Comput 1:69–83
Hromkovic J, Muller V, Sykora O, Vrt’o I (1995) On embeddings in cycles. Inf Comput 118:302–305
Ishibuchi H, Narukawa K (2004) Some issues on the implementation of local search in evolutionary multi-objective optimization. Proc GECCO 1:1246–1258
Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multi-objective permutation flowshop scheduling. IEEE Trans Evol Comput 7(2):204–223
Leung JYT, Vornberger O, Witthoff JD (1984) On some variants of the bandwidth minimization problem. SIAM J Computing 13:650–667
Lim MH, Gustafson S, Krasnogor N, Ong YS (2009) Editorial to the first issue. Memetic Comput 1:1–2
Lin Y (1997) Minimum bandwidth problem for embedding graphs in cycles. NETWORKS 29:135–140
Miller Z, Pritikin D (1989) On the separation number of a graph. Networks 19:651–666
Molina D, Lozano M, Herrera F (2009) A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions. In: Proceedings of 11th annual conference on GECCO, pp 2359–2366
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. In: Caltech concurrent computation program, C3P report 826
Neri F, Toivanen J, Cascella GL, Ong YS (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinf 4(2):264–278
Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36(1):141–152
Ong YS, Krasnogor N, Ishibuchi H (eds) (2007) Special issue on memetic algorithms. IEEE Trans Syst Man Cybern B 37(1):2–5
Raspaud A, Schroder H, Sykora O, Torok L, Vrt’o I (2009) Antibandwidth and cyclic antibandwidth of meshes and hypercubes. Discret Math 309:3541–3552
Sharma R, Srivastava K (2009) A new hybrid evolutionary algorithm for the MinLA problem. Int J Oper Res 5(2):229–249
Sykora O, Torok L, Vrt’o I (2005) The cyclic antibandwidth problem. Electron Notes Discret Math 22:223–227
Tang M, Yao X (2007) A memetic algorithm for VLSI floorplanning. IEEE Trans Syst Man Cybern B 37(1):62–69
Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput 11(9):873–888
Wang X, Wu X, Dumitrescu S (2009) On explicit formulas for bandwidth and antibandwidth of hypercubes. Discret Appl Math 157(8):1947–1952
Wang H, Wang D, Yang S (2009) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput 13(8–9):763–780
Zhou Z, Ong YS, Lim MH, Lee BS (2007) Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Comput 11(10):957–971 GDToolKit: http://www.dia.uniroma3.it/~gdt/
Acknowledgments
This work is supported by UGC vide letter no 36-66/2008 (SR). The authors would like to thank anonymous referees for their valuable comments and suggestions which have helped improve the quality and readability of the paper.
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Bansal, R., Srivastava, K. A memetic algorithm for the cyclic antibandwidth maximization problem. Soft Comput 15, 397–412 (2011). https://doi.org/10.1007/s00500-009-0538-6
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DOI: https://doi.org/10.1007/s00500-009-0538-6