Multi-objective layout optimization of a satellite module using the Wang-Landau sampling method with local search<FootNote> Project supported by the National Natural Science Foundation of China (Nos. 61373016 and 61403206), the Six Talent Peaks Project of Jiangsu Province, China (No. DZXX-041), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Natural Science Foundation of Jiangsu Province, China (No. BK20141005) </FootNote>

Jing-fa LIU, Liang HAO, Gang LI, Yu XUE, Zhao-xia LIU, Juan HUANG

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Front. Inform. Technol. Electron. Eng ›› 2016, Vol. 17 ›› Issue (6) : 527-542. DOI: 10.1631/FITEE.1500292
Article
Article

Multi-objective layout optimization of a satellite module using the Wang-Landau sampling method with local search<FootNote> Project supported by the National Natural Science Foundation of China (Nos. 61373016 and 61403206), the Six Talent Peaks Project of Jiangsu Province, China (No. DZXX-041), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Natural Science Foundation of Jiangsu Province, China (No. BK20141005) </FootNote>

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Abstract

The layout design of satellite modules is considered to be NP-hard. It is not only a complex coupled system design problem but also a special multi-objective optimization problem. The greatest challenge in solving this problem is that the function to be optimized is characterized by a multitude of local minima separated by high-energy barriers. The Wang-Landau (WL) sampling method, which is an improved Monte Carlo method, has been successfully applied to solve many optimization problems. In this paper we use the WL sampling method to optimize the layout of a satellite module. To accelerate the search for a global optimal layout, local search (LS) based on the gradient method is executed once the Monte-Carlo sweep produces a new layout. By combining the WL sampling algorithm, the LS method, and heuristic layout update strategies, a hybrid method called WL-LS is proposed to obtain a final layout scheme. Furthermore, to improve significantly the efficiency of the algorithm, we propose an accurate and fast computational method for the overlapping depth between two objects (such as two rectangular objects, two circular objects, or a rectangular object and a circular object) embedding each other. The rectangular objects are placed orthogonally. We test two instances using first 51 and then 53 objects. For both instances, the proposed WL-LS algorithm outperforms methods in the literature. Numerical results show that the WL-LS algorithm is an effective method for layout optimization of satellite modules.

Keywords

Packing / Layout design / Satellite module / Wang-Landau algorithm

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Jing-fa LIU, Liang HAO, Gang LI, Yu XUE, Zhao-xia LIU, Juan HUANG. Multi-objective layout optimization of a satellite module using the Wang-Landau sampling method with local search<FootNote> Project supported by the National Natural Science Foundation of China (Nos. 61373016 and 61403206), the Six Talent Peaks Project of Jiangsu Province, China (No. DZXX-041), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Natural Science Foundation of Jiangsu Province, China (No. BK20141005) </FootNote>. Front. Inform. Technol. Electron. Eng, 2016, 17(6): 527‒542 https://doi.org/10.1631/FITEE.1500292

References

[1]
Allen, S.D., Burke, E.K., Kendall, G., 2011. A hybrid placement strategy for the three-dimensional strip packing problem. Eur. J. Oper. Res., 209(3):219–227. http://dx.doi.org/10.1016/j.ejor.2010.09.023
[2]
Bennell, J.A., Dowsland, K.A., Dowsland, W.B., 2001. The irregular cutting-stock problem—a new procedure for deriving the no-fit polygon. Comput. Oper. Res., 28(3): 271–287. http://dx.doi.org/10.1016/S0305-0548(00)00021-6
[3]
Chen, W., Shi, Y.J., Teng, H.F., 2008. An improved differential evolution with local search for constrained layout optimization of satellite module. LNCS, 5227:742–749. http://dx.doi.org/10.1007/978-3-540-85984-0_89
[4]
Crainic, T.G., Perboli, G., Rei, W., et al., 2011. Efficient lower bounds and heuristics for the variable cost and size bin packing problem. Comput. Oper. Res., 38(11):1474–1482. http://dx.doi.org/10.1016/j.cor.2011.01.001
[5]
Gonçalves, J.F., Resende, M.G., 2011. A parallel multipopulation genetic algorithm for a constrained twodimensional orthogonal packing problem. J. Comb. Optim., 22(2):180–201. http://dx.doi.org/10.1007/s10878-009-9282-1
[6]
He, K., Mo, D.Z., Xu, R.C., et al., 2013. A quasi-physical algorithm based on coarse and fine adjustment for solving circles packing problem with constraints of equilibrium. Chin. J. Comput., 36(6):1224–1234 (in Chinese). http://dx.doi.org/10.3724/SP.J.1016.2013.01224
[7]
Huang, W.Q., Kang, Y., 2004. A short note on a simple search heuristic for the diskspacking problem. Ann. Oper. Res., 131(1-4):101–108. http://dx.doi.org/10.1023/B:ANOR.0000039514.14699.03
[8]
Huo, J.Z., Teng, H.F., 2009. Optimal layout design of a satellite module using a coevolutionary method with heuristic rules. J. Aerosp. Eng., 22(2):101–111. http://dx.doi.org/10.1061/(ASCE)0893-1321(2009)22:2 (101)
[9]
Jin, B., Teng, H.F., 2007. Case-based evolutionary design approach for satellite module layout. J. Sci. Ind. Res., 66(12):989–994.
[10]
Khanafer, A., Clautiaux, F., Talbi, E.G., 2012. Treedecomposition based heuristics for the two-dimensional bin packing problem with conflicts. Comput. Oper. Res., 39(1):54–63. http://dx.doi.org/10.1016/j.cor.2010.07.009
[11]
Landau, D.P., Tsai, S.H., Exler, M., 2004. A new approach to Monte Carlo simulations in statistical physics: Wang-Landau sampling. Am. J. Phys., 72(10):1294–1302. http://dx.doi.org/10.1119/1.1707017
[12]
Lei, K.Y., Qiu, Y.H., 2006. A study of constrained layout optimization using adaptive particle swarm optimizer. J. Comput. Res. Dev., 43:1724–1731 (in Chinese).
[13]
Li, Z.Q., 2010. A fast projection-separation approach for collision detection between polytopes. J. Comput. Aid. Des. Comput. Graph., 22(4):639–646 (in Chinese).
[14]
Li, Z.Q., Xie, Y.F., Tian, Z.J., et al., 2012. A heuristic particle swarm optimization with quasi-human strategy for weighted circles packing problem. 8th Int. Conf. on Natural Computation, p.723–727. http://dx.doi.org/10.1109/ICNC.2012.6234660
[15]
Liu, J.F., Li, G., 2010. Basin filling algorithm for the circular packing problem with equilibrium behavioral constraints. Sci. Chin. Inf. Sci., 53(5):885–895 (in Chinese). http://dx.doi.org/10.1007/s11432-010-0080-2
[16]
Liu, J.F., Xue, S.J., Liu, Z.X., et al., 2009. An improved energy landscape paving algorithm for the problem of packing circles into a larger containing circle. Comput. Ind. Eng., 57(3):1144–1149. http://dx.doi.org/10.1016/j.cie.2009.05.010
[17]
Liu, J.F., Li, G., Chen, D.B., et al., 2010. Two-dimensional equilibrium constraint layout using simulated annealing. Comput. Ind. Eng., 59(4):530–536. http://dx.doi.org/10.1016/j.cie.2010.06.009
[18]
Liu, J.F., Li, G., Geng, H.T., 2011. A new heuristic algorithm for the circular packing problem with equilibrium constraints. Sci. Chin. Inf. Sci., 54(8):1572–1584. http://dx.doi.org/10.1007/s11432-011-4351-3
[19]
Liu, Z.W., Teng, H.F., 2008. Human–computer cooperative layout design method and its application. Comput. Ind. Eng., 55(4):735–757. http://dx.doi.org/10.1016/j.cie.2006.11.007
[20]
Martello, S., Pisinger, D., Vigo, D., 2000. The threedimensional bin packing problem. Oper. Res., 48(2):256–267. http://dx.doi.org/10.1287/opre.48.2.256.12386
[21]
Moon, I., Nguyen, T.V.L., 2014. Container packing problem with balance constraints. OR Spectr., 36(4):837–878. http://dx.doi.org/10.1007/s00291-013-0356-1
[22]
Schulz, B.J., Binder, K., Müller, M., et al., 2003. Avoiding boundary effects in Wang-Landau sampling. Phys. Rev. E, 67(6):067102. http://dx.doi.org/10.1103/PhysRevE.67.067102
[23]
Seaton, D.T., Wüst, T., Landau, D.P., 2010. Collapse transitions in a flexible homopolymer chain: application of the Wang-Landau algorithm. Phys. Rev. E, 81(1):011802. http://dx.doi.org/10.1103/PhysRevE.81.011802
[24]
Sun, Z.G., Teng, H.F., 2003. Optimal layout design of a satellite module. Eng. Optim., 35(5):513–529. http://dx.doi.org/10.1080/03052150310001602335
[25]
Tang, F., Teng, H.F., 1999. A modified genetic algorithm and its application to layout optimization. J. Softw., 10(10): 1096–1102 (in Chinese).
[26]
Teng, H.F., Chen, Y., Zeng, W., et al., 2010. A dual-system variable-grain cooperative coevolutionary algorithm: satellite-module layout design. IEEE Trans. Evol. Comput., 14(3):438–455. http://dx.doi.org/10.1109/TEVC.2009.2033585
[27]
Wang, F., Landau, D.P., 2001. Efficient, multiple-range random walk algorithm to calculate the density of states. Phys. Rev. Lett., 86(10):2050. http://dx.doi.org/10.1103/PhysRevLett.86.2050
[28]
Wang, Y.S., Teng, H.F., 2009. Knowledge fusion design method: satellite module layout. Chin. J. Aeronaut., 22(1): 32–42. http://dx.doi.org/10.1016/S1000-9361(08)60066-7
[29]
Wu, M.H., Yu, Y.X., Zhou, J., 1997. An octree algorithm for collision detection using space partition. Chin. J. Comput., 20(9):849–854 (in Chinese).
[30]
Xu, Y.C., Xiao, R.B., 2008. Ant colony algorithm for layout optimization with equilibrium constraints. Contr. Dec., 23(1):25–29 (in Chinese).
[31]
Zhang, B., Teng, H.F., Shi, Y.J., 2008. Layout optimization of satellite module using soft computing techniques. Appl. Soft Comput., 8(1):507–521. http://dx.doi.org/10.1016/j.asoc.2007.03.004
[32]
Zhang, D.F., Deng, A.S., Kang, Y., 2005. A hybrid heuristic algorithm for the rectangular packing problem. Int. Conf. on Computational Science, p.783–791. http://dx.doi.org/10.1007/11428831_97
[33]
Zhang, Z.H., Wang, Y.S., Teng, H.F., et al., 2013. Parallel dual-system cooperative co-evolutionary differential evolution algorithm with human-computer cooperation for multi-cabin satellite layout optimization. J. Converg. Inform. Technol., 8(4):711–720. http://dx.doi.org/10.4156/jcit.vol8.issue4.82
[34]
Zhou, C., Bhatt, R.N., 2005. Understanding and improving the Wang-Landau algorithm. Phys. Rev. E, 72(2):025701. http://dx.doi.org/10.1103/PhysRevE.72.025701
[35]
Zhou, C., Gao, L., Gao, H.B., 2005. Particle swarm optimization based algorithm for constrained layout optimization. Contr. Dec., 20(1):36–40 (in Chinese).

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