Genetic Algorithm with Modified Crossover for Grillage Optimization
Keywords:
genetic algorithm, crossover operator, grillage optimizationAbstract
Modified genetic algorithm with special phenotypes' selection and crossover operators with default specified rules is proposed in this paper thus refusing the random crossover. The suggested crossover operator enables wide distribution of genes of the best phenotypes over the whole population. During selection and crossover, the best phenotypes of the newest population and additionally the genes of the best individuals of two previous populations are involved. The effectiveness of the modified algorithm is shown numerically on the real-life global optimization problem from civil engineering - the optimal pile placement problem under grillage-type foundations. This problem is a fair indicator for global optimization algorithms since the ideal solutions are known in advance but with unknown magnitudes of design parameters. Comparison of the proposed algorithm with 6 other stochastic optimization algorithms clearly reveals its advantages: at similar accuracy level the algorithm requires less time for tuning of genetic parameters and provides narrower confidence intervals on the results than other algorithms.References
Belevicius R., Ivanikovas S., Sesok D., Zilinskas J., Valentinavicius S. (2011);
Optimal placement of piles in real grillages: experimental comparison of optimization algorithms, Information Technology and Control, 40(2), 123-132, 2011.
Belevicius R., Valentinavicius S. (2001); Optimisation of grillage-type foundations, Proceedings of 2nd European ECCOMAS and IACM Conference Solids, Structures and Coupled Problems in Engineering, 416-421, Cracow, Poland 26-29 June, 2001.
Belevicius R., Valentinavicius S., Michnevi E. (2002); Multilevel optimization of grillages, Journal of Civil Engineering and Management, http://dx.doi.org/10.1080/13923730.2002.10531259, 8(2), 98-103, 2002. https://doi.org/10.1080/13923730.2002.10531259
De Jong K.A., Spears W.M. (1992); A formal analysis of the role of multi-point crossover in genetic algorithms, Annals of Mathematics and Artificial Intelligence, 5(1), 1-26, 1992. https://doi.org/10.1007/BF01530777
Garci C.M., Lozano M., Herrera F., Molina, D., Sanchez A.M. (2008); Global and local realcoded genetic algorithms based on parent centric crossover operators, European Journal of Operational Research, 185, 1088-1113, 2008. https://doi.org/10.1016/j.ejor.2006.06.043
Kim K.N., Lee S.-H., Kim K.-S., Chung C.-K., Kim M.M., Lee H.S. (2001); Optimal pile arrangement for minimizing differential settlements in piled raft foundations, Computers and Geotechnics, http://dx.doi.org/10.1016/S0266352X(01)00002-7, 28(4), 235-253, 2001.
Kita H. (2001); A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms, Evolutionary Computation Journal, 9(2), 223-241, 2001. https://doi.org/10.1162/106365601750190415
Mockus J., Belevicius R., Sesok D., Kaunas J., Maciunas D. (2012); On Bayesian approach to grillage optimization, Information Technology and Control, 41(4), 332-339, 2012.
Mockus J., Eddy W., Mockus A., Mockus L., Reklaitis G. (1997); Bayesian Heuristic Approach to Discrete and Global Optimization, Kluwer Academic Publishers, ISBN 0-7923- 43227-1, Dordrecht-London-Boston, 1997.
Nelder J. A., Mead R. (1965); A simplex method for function minimization, Computer Journal, 7, 308-313, 1965. https://doi.org/10.1093/comjnl/7.4.308
Oliver I.M., Smith D.J., Holland J.R.C. (1987); A study of permutation crossover operators on the traveling salesman problem, In Proceedings of the Second International Conference on Genetic Algorithms, Mahwah, NJ, USA, 1987. Lawrence Erlbaum Associates, Inc. sd., 224-230, 1987.
Powell M.J.D. (2006); The NEEWUOA software for unconstrained optimization without derivatives, Di Pillo, G., Roma, M. (eds) Large-Scale Nonlinear Optimization. Vol. 83 of Nonconveex Optimization and Its Applications, Springer, 255-296, 2006.
Reese L.C., Isenhhower W.M., Wang S.-T. (2006); Analysis and Design of Shallow and Deep Foundations, John Wiley & Sons, 2006.
Ros R. (2009); Benchmarking the NEWUOA on the BBOB-2009 noisy testbed, F. Rothlauf, editor, GECCO (Companion), ACM, 2429-2434, 2009. https://doi.org/10.1145/1570256.1570339
Watson J.-P., Ross C., Eisele V., Denton J., Bins J., Guerra C., Whitley L.D., Howe A.E. (1998); The traveling salesrep problem, edge assembly crossover, and 2-opt, PPSN V: Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, London, UK, Springer-Verlag, 823-834, 1998.
Zienkiewicz O.C., Taylor R.L., Nithiarasu P. (2005); The Finite Element Method for Fluid Dynamics, Butterworth-Heinemann, Oxford, 6th edition, 2005.
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