Abstract
In this paper, the optimal design of grillage-type foundations is considered. A difficult black-box global optimization problem should be solved to find the optimal positions of piles in this type of foundations. The goal of the research was to enable the solution of real grillage problems by distributed (volunteer) computing using the open infrastructure for network computing BOINC. A distributed genetic algorithm has been developed as well as its implementation on a computational BOINC platform. The algorithm is adapted to suit the BOINC platform and cope with possible occasional faults of client computers. The results of the proposed algorithm are compared with those attained by using the simulated annealing algorithm running on a computational grid infrastructure which previously showed the best performance. The results on 10 real grillage foundations and quantitative comparison reveal that the performance of the proposed algorithm is better.
Similar content being viewed by others
References
Allen, B., et al.: The Einstein@Home search for radio pulsars and PSR J2007+2722 discovery. Astrophys. J. 773(2), 91 (2013)
Anderson, D.P.: BOINC: a system for public resource computing and storage. In: Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing, pp. 1–7 (2004). https://doi.org/10.1109/GRID.2004.14
Baravykaitė, M., Belevičius, R., Čiegis, R.: One application of the parallelization tool of Master–Slave algorithms. Informatica 13(4), 393–404 (2002)
Belevičius, R., Šešok, D.: Global optimization of grillages using genetic algorithms. Mechanika 6(74), 38–44 (2008)
Belevičius, R., Mačiūnas, D.,Šešok, D.: Momentu̧ ir reakciju̧ minimizavimasrostverkiniuose pamatu̧ sijynuose genetiniu algoritmu.Statybinės konstrukcijos ir technologijos = Engineeringstructures and technologies 3(2), 56–63 (2011)
Belevičius, R., Valentinavičius, S.: Optimization of grillage-type foundations. J. Civ. Eng. Manag. 6(6), 255–261 (2000)
Bowles, J.E.: Foundation Analysis and Design, 5th edn. The McGraw-Hill Companies Inc, New York (1996)
Čiegis, R., Baravykaite, M., Belevičius, R.: Parallel global optimisation of foundation schemes in civil engineering. Lecture Notes in Computer Science. In: Dongarra J., Madsen K., Wasniewski J. (eds.) 7th International Conference on Applied Parallel Computing: State of the Art in Scientific Computing, PARA 2004, Lyngby, Denmark, June 20–23. Revised Selected Papers. vol. 3732, pp. 305–312. Springer, Berlin (2006)
Čiegis, R., Starikovičius, V., Tumanova, N., Ragulskis, M.: Application of distributed parallel computing for dynamic visual cryptography. J. Supercomput. 72(11), 4204–4220 (2016). https://doi.org/10.1007/s11227-016-1733-8
Corcoran, A.L., Wainwright, R.L.: A parallel island model genetic algorithm for the multiprocessor scheduling problem. In: Proceedings of the 1994 ACM Symposium on Applied Computing, 483-487, (1994)
Ferrucci, F., Salza, P., Sarro, F.: Using Hadoop MapReduce for parallel genetic algorithms: a comparison of the global, grid and island models. Evolut. Comput. 26(4), 535–567 (2017)
Kim, K.N., Lee, S.H., Kim, K.S., Chung, C.K., Kim, M.M., Lee, H.S.: Optimal pile arrangement for minimizing differential settlements in piled raft foundations. Comput. Geotech. 28, 235–253 (2001)
Korpela, E.J.: SETI@Home, BOINC, and volunteer distributed computing. Annu. Rev. Earth Planet. Sci. 40(1), 69–87 (2012)
Kripka, M., Stochastic optimization applied to R-C building grillages. In: Proceedings of 6th World Congress of Structural and Multidisciplinary Optimization. CD-ROM, Rio de Janeiro, Brazil, 30 May–03 June (2005)
Luque, G., Alba, E.: Parallel Genetic Algorithms: Theory and Real World Applications. Studies in Computational Intelligence, vol. 367. Springer, Berlin (2011)
Muszynski, J.: Cheating-Tolerance of Parallel and Distributed Evolutionary Algorithms in Desktop Grids and Volunteer Computing Systems. Thesis (2015)
Nogueras, R., Cotta, C.: Analyzing resilience to computational glitches in island-based evolutionary algorithms. In: Auger, A., Fonseca, C., Lourenco, N., Machado, P., Paquete, L., Whitley, D. (eds.) Parallel Problem Solving from Nature–PPSN XV PPSN 2018. Lecture Notes in Computer Science, vol. 11101, pp. 411–423 (2018)
Ramanauskas, M., Šešok, D., Belevičius, R., Kurilov, J., Valentinavičius, S.: Genetic algorithm with modified crossover for grillage optimization. Int. J. Comput. Commun. Control 12(3), 393–401 (2017)
Reese, L.C., Isenhower, W.M., Wang, S.-T.: Analysis and Design of Shallow and Deep Foundations. Wiley, Hoboken (2005)
Šešok, D., Belevičius, R., Kačeniauskas, A., Mockus, J.: Application of GRID computing for optimization of grillages. Mechanika 2(82), 63–69 (2010)
Šešok, D., Mockus, J., Belevičius, R., Kačeniauskas, A.: Global optimization of grillages using simulated annealing and high performance computing. J. Civ. Eng. Manag. 16(1), 95–101 (2010)
Zienkiewicz, O.C., Taylor, R.L., Zhu, J.Z.: The Finite Element Method: Its Basis and Fundamentals, 6th edn. Butterworth-Heinemann, Oxford (2005)
Žilinskas, J.: Branch and bound with simplicial partitions for global optimization. Math. Model. Anal. 13(1), 145–159 (2008). https://doi.org/10.3846/1392-6292.2008.13.145-159
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ramanauskas, M., Šešok, D., Žilinskas, J. et al. Global optimization of grillage-type foundations using a distributed genetic algorithm. J Glob Optim 77, 157–173 (2020). https://doi.org/10.1007/s10898-019-00838-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10898-019-00838-2