A Hybrid NSGA-II Algorithm for Multiobjective Quadratic Assignment Problems

• Authors: Z. Kamisli Ozturk , M. Uluel
• Languages of publication: EN

Abstracts

EN

In this study, we propose a novel hybrid multiobjective evolutionary algorithm for solving multiobjective quadratic assignment problems. During the last decade, the researchers gave increasing attention to the multiobjective structure of quadratic assignment problems and developed and/or used several multi objective metaheuristics. The nondominated sorting genetic algorithm (NSGA-II) has been shown to solve various multiobjective problems much better than other recently-proposed constraint handling approaches. Besides, the effectiveness of conic scalarization method was also proven for solution of multiobjective problems, that have non-linear structure. Here, a hybrid multiobjective evolutionary algorithm (cNSGA-II) featured with NSGA-II and conic scalarization's Pareto solutions is developed to obtain as much Pareto points, as possible. To test the performance of the algorithm we have selected the test problems from the literature and compared the performances by well-known diameter metric. It has been shown that cNSGA-II is effective in solving multiobjective quadratic assignment problems.

Keywords

EN

Hybrid NSGA-II; Multiobjective Quadratic Assignment; Conic Scalarization

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2017
• Volume: 132
• Issue: 3
• Pages: 959-962

Dates

published

2017-09

Contributors

author

Z. Kamisli Ozturk

• Anadolu University, Industrial Engineering Department, Eskisehir, Turkey

author

M. Uluel

• TUSAS Engine Industry, Eskisehir, Turkey

References

• [1] T.C. Koopman, B. Beckmann, Econometrica, 53 1 (1957)
• [2] J. Knowles, D. Corne, in: Soft Computing Systems: Design, Management and Applications, Ed. A. Abraham, IOS Press, Amsterdam 2002, p. 271
• [3] İ. H. Karahan, R. Özdemir, Acta Phys. Pol. A 128, B-427 (2015), doi: 10.12693/APhysPolA.128.B-427
• [4] E. Kanca, F. Çavdar, M.M. Erşen, Acta Phys. Pol. A 130, 365 (2016), doi: 10.12693/APhysPolA.130.365
• [5] A.T. Özturhan, Acta Phys. Pol. A 130, 14 (2016), doi: 10.12693/APhysPolA.130.14
• [6] P. Stefanov, A. Savic, G. Dobric, Acta Phys. Pol. A 128, B-138 (2015), doi: 10.12693/APhysPolA.128.B-138
• [7] A. Recioui, Acta Phys. Pol. A 128, B-7 (2015), doi: 10.12693/APhysPolA.128.B-7
• [8] J. Knowles, D. Corne, Instance generator and test suites for the mQAP, Evolutionary Multi-Criterion Optimization (EMO), in: Second International Conference, Portugal 2003
• [9] D. Garrett, D. Dasgupta, in: IEEE Congress on Evolutionary Computation, Vancouver 2006, p. 1710
• [10] D. Garrett, D. Dasgupta, in: IEEE Symposium on Computational Intelligence in Multi Criteria Decision Making, Nashville 2009, p. 80
• [11] L. Paquete, T. Stützle, Eur. J. Operat. Res. 169, 943 (2006), doi: 10.1016/j.ejor.2004.08.024
• [12] H. Li, D. Silva, LNCS 5467, 481 (2009)
• [13] C. Özkale, A. Fığlalı, Appl. Math. Model. 37, 7822 (2013), doi: 10.1016/j.apm.2013.01.045
• [14] C.P. Almeida, E.F. Goldberg, M.R. Delgado, in: Brazilian Conf. Intelligent Systems, IEEE, 2014, p. 312
• [15] M. Zhao, A. Abraham, C. Grosan, H. Liu, in: 2nd Asia Int. Conf. Modelling & Simulation, IEEE, 2008, p. 516
• [16] E.E. Ammar, Inform. Sci. 178, 468 (2008), doi: 10.1016/j.ins.2007.03.029
• [17] E.E. Ammar, Eur. J. Operat. Res. 193, 329 (2009), doi: 10.1016/j.ejor.2007.11.031
• [18] R. Kasimbeyli, Z.K. Öztürk, N. Kasimbeyli, G.D. Yalçin, B. Içmen, Conic scalarization method in multiobjective optimization and relations with other scalarization methods, in: Modelling, Computation and Optimization in Information Systems and Management Sciences, Proceedings of MCO 2015, Eds. L.T.H. An, P.D. Tao, N.N. Thanh, vol. 359, part V, Springer, 2015
• [19] E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm, TIK-Report 103, 2001
• [20] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, IEEE Trans. Evolut. Comput. 6, 182 (2002), doi: 10.1109/4235.996017
• [21] R.O. Day, G.B. Lamont, Multiobjective quadratic assignment problem solved by an explicit building block search algorithm MOMGA-IIa, LNCS 3448, Springer-Verlag, Berlin Heidelberg, p. 91
• [22] M.J. Pazdanowski, Acta Phys. Pol. A 128, B-213 (2015), doi: 10.12693/APhysPolA.128.B-213

Identifiers

DOI

10.12693/APhysPolA.132.959