Abstract
This paper concerns with solving nonlinear boundary value problems with Dirichlet boundary conditions by a novel approximation technique based on Bernstein polynomials and meta-heuristic algorithms. A trial solution is expressed as a weighted sum of Bernstein polynomials of degree \(n\). Some weights of this trial solution are determined by enforcing exact satisfaction of the Dirichlet boundary conditions. The remaining weights are determined such that they minimize the sum of squares of the residuals of the differential equation computed at arbitrary set of interior points in the domain of the problem. Error bounds for the approximate solutions for ordinary and partial differential equations are derived. The resulting optimization problem is solved using meta-heuristic algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and a hybrid PSO–GA algorithm. The accuracy of the proposed approach is demonstrated by solving some nonlinear boundary value problems including Bratu problems in one- and two-dimensional spaces.
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References
Abbasbandy S, Shivanian E (2010) Prediction of multiplicity of solutions of nonlinear boundary value problems: Novel application of homotopy analysis method. Commun Nonlinear Sci Numer Simul 15:3830–3846
Abd-El-Wahed W, Mousa A, El-Shorbagy M (2011) Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems J. Comput Appl Math 235:1446–1453
Abo-bakr RM, Mujeed TA (2015) Solving nonlinear constrained optimization problems using hybrid evolutionary algorithms. 2015, 11th International Computer Engineering Conference (ICENCO 2015)
Ahmed HM (2014) Solutions of 2nd-order linear differential equations subject to Dirichlet boundary conditions in a Bernstein polynomial basis. J Egyptian Math Soc 22:227–237
Ascher UM, Matheij RMM, Russell RD (1995) Numerical solution of boundary value problems for ordinary differential equations. Society for Industrial and Applied Mathematics, Philadelphia, PA
Bhatti MI, Bracken P (2007) Solutions of differential equations in a Bernstein polynomial basis. J Comput Appl Math 205:272–280
Boyd JP (1986) An analytical and numerical study of the two-dimensional Bratu equation. J Sci Comput 1:183–206
Boyd JP (2003) Chebyshev polynomial expansions for simultaneous approximation of two branches of a function with application to the one dimensional Bratu equation. Appl Math Comput 142:189–200
Boyd JP (2011) One-point pseudospectral collocation for the one-dimensional Bratu equation. Appl Math Comput 217:5553–5565
Doha EH, Bhrawy AH, Saker MA (2011) Integrals of Bernstein polynomials: an application for the solution of high even-order differential equations. Appl Math Lett 24:559–565
Eberhart R and Kennedy J (1995) A new optimizer using particle swarm theory. Microm. and Human Sc., the Sixth International Symposium on, Nagoya, Japan pp 39–43
Farin G (1996) Curves and surfaces for computer aided geometric design. Academic Press, Boston
Farouki RT, Goodman TNT (1996) On the optimal stability of the Bernstein basis. Math Comput 65(216):1553–1566
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor, MI
Jacobsen J, Schmitt K (2002) The Liouville–Bratu–Gelfand problem for radial operators. J Differ Equ 184:283–298
Li S, Liao S (2005) An analytic approach to solve multiple solutions of a strongly nonlinear problem. Appl Math Comput 169:854–865
Lorentz GG (1986) Bernstein polynomials. Chelsea Publishing Company, New York
Maleknejad K, Hashemizadeh E, Ezzati R (2011) A new approach to the numerical solution of Volterra integral equations by using Bernstein’s approximation. Commun Nonlinear Sci Numer Simul 16:647–655
Maleknejad K, Basirat B, Hashemizadeh E (2012a) A Bernstein operational matrix approach for solving a system of high order linear Volterra-Fredholm integro-differential equations. Math Comput Model 55:1363–1372
Maleknejad K, Hashemizadeh E, Basirat B (2012b) Computational method based on Bernstein operational matrices for nonlinear Volterra–Fredholm–Hammerstein integral equations. Commun Nonlinear Sci Numer Simul 17(1):52–61
Mandal BN, Bhattacharya S (2007) Numerical solution of some classes of integral equations using Bernstein polynomials. Appl Math Comput 190:707–1716
Mirzaee F, Samadyar N (2018a) Parameters estimation of HIV infection model of CD4+ T-cells by applying orthonormal Bernstein collocation method. Int J Biomath 11(02):1850020
Mirzaee F, Samadyar N (2018b) Application of hat basis functions for solving two-dimensional stochastic fractional integral equations. Comput Appl Math 37(4):4899–4916
Mirzaee F, Samadyar N (2018c) On the numerical solution of stochastic quadratic integral equations via operational matrix method. Math Methods Appl Sci 41(12):4465–4479
Mirzaee F, Samadyar N (2021) Implicit meshless method to solve 2D fractional stochastic Tricomi-type equation defined on irregular domain occurring in fractal transonic flow. Numer Methods Partial Differ Equ 37(2):1781–1799
Mirzaee F, Samadyar N, Alipour S (2019a) Numerical solution of high order linear complex differential equations via complex operational matrix method. SeMA Journal 76(1):1–13
Mirzaee F, Alipour S, Samadyar N (2019b) Numerical solution based on hybrid of block-pulse and parabolic functions for solving a system of nonlinear stochastic Itô-Volterra integral equations of fractional order. J Comput Appl Math 349:157–171
Mirzaee F and Samadyar N (2019) Application of Bernoulli wavelet method for estimating a solution of linear stochastic Itô-Volterra integral equations. Multidiscipline Modeling in Materials and Structures
Mohsen A (2013) On the integral solution of the one-dimensional Bratu problem. J Comput Appl Math 251:61–66
Mohsen A (2014) A simple solution of the Bratu problem. Comput Math Appl 67:26–33
Mohsen A, Sedeek LF, Mohamed SA (2008) New smoother to enhance multigrid-based methods for Bratu problem. Appl Math Comput 204:325–339
Ng KKH, Lee CK, Chan FT, Lv Y (2018) Review on meta-heuristics approaches for airside operation research. Appl Soft Comput 66:104–133
Noel MM (2012) A new gradient based particle swarm optimization algorithm for accurate computation of global minimum. Appl Soft Comput 12(1):353–359
Perez RE, Behdinan K (2007) Particle swarm approach for structural design optimization. Comput Struct 85:1579–1588
Phillips GM (2003) Interpolation and approximation by polynomials. Springer, Berlin
Raja MAZ, Ahmad SI, Samar R (2013) Neural network optimized with evolutionary computing technique for solving the 2-dimensional Bratu problem. Neural Comput Appl 23(7):2199–2210
Rivlin TJ (1969) An introduction to the approximation of functions. Dover Publications, New York
Samadyar N, Mirzaee F (2019) Numerical scheme for solving singular fractional partial integro-differential equation via orthonormal Bernoulli polynomials. Int J Numer Model: Electron Netw, Devices Fields 32(6):e2652
Samadyar N, Mirzaee F (2020) Orthonormal Bernoulli polynomials collocation approach for solving stochastic Itô-Volterra integral equations of Abel type. Int J Numer Model: Electron Netw, Devices Fields 33(1):e2688
Syam M, Hamdan A (2006) An efficient method for solving Bratu equations. Appl Math Comput 176:704–713
Wazwaz AM (2005) Adomian decomposition method for a reliable treatment of the Bratu-type equations. Appl Math Comput 166:652–663
Yousefi SA, Behroozifar M (2010) Operational matrices of Bernstein polynomials and their applications. Internat J Systems Sci 41(6):709–716
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Abo-bakr, R.M., Mohamed, N.A. & Mohamed, S.A. Meta-heuristic algorithms for solving nonlinear differential equations based on multivariate Bernstein polynomials. Soft Comput 26, 605–619 (2022). https://doi.org/10.1007/s00500-021-06535-1
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DOI: https://doi.org/10.1007/s00500-021-06535-1