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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2017

19.01.2016 | Original Article

Biologically inspired computing framework for solving two-point boundary value problems using differential evolution

verfasst von: Muhammad Faisal Fateh, Aneela Zameer, Nasir M. Mirza, Sikander M. Mirza, Muhammad Asif Zahoor Raja

Erschienen in: Neural Computing and Applications | Ausgabe 8/2017

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Abstract

In the present study, a design of biologically inspired computing framework is presented for solving second-order two-point boundary value problems (BVPs) by differential evolution (DE) algorithm employing finite difference-based cost function. The DE has been implemented to minimize the combined residue from all nodes in a least square sense. The proposed methodology has been evaluated using five numerical examples in linear and nonlinear regime of BVPs in order to demonstrate the process and check the efficacy of the implementation. The assessment and validation of the DE algorithm have been carried out by comparing the DE-computed results with exact solution as well as with the corresponding data obtained using continuous genetic algorithms. These benchmark comparisons clearly establish DE as a competitive solver in this domain in terms of computational competence and precision.
Vorheriger Artikel A general description generator for human activity images based on deep understanding framework
Nächster Artikel Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer

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Literatur
1.
2.
3.
Saadatmandi A, Razzaghi M, Dehghan M (2005) Sinc-Galerkin solution for nonlinear two-point boundary value problems with applications to chemical reactor theory. Math Comput Model 42:1237–1244MathSciNetCrossRefMATH
4.
Arnold A, Holm C (2005) MMM1D: a method for calculating electrostatic interactions in one-dimensional periodic geometries. J Chem Phys 123:144103CrossRef
5.
Levrel L, Maggs AC (2008) Boundary conditions in local electrostatics algorithms. J Chem Phys 128:214103CrossRef
6.
Yeh IC, Wallqvist A (2011) On the proper calculation of electrostatic interactions in solid-supported bilayer systems. J Chem Phys 134:055109CrossRef
7.
Cheng W, Fu C-L (2009) Solving the axisymmetric inverse heat conduction problem by a wavelet dual least squares method. Bound Value Probl 2009:1–13MathSciNetCrossRefMATH
8.
Chai G (2011) Positive solution of fourth-order integral boundary value problem with two parameters. Abstr Appl Anal 2011:1–19MathSciNetCrossRef
9.
Salva N, Tarzia D, Villa L (2011) An initial-boundary value problem for the one-dimensional non-classical heat equation in a slab. Bound Value Probl 2011:4MathSciNetCrossRefMATH
10.
11.
Radulescu O, Lagarrigue S, Siegel A, Veber P, Le Borgne M (2006) Topology and static response of interaction networks in molecular biology. J R Soc Interface 3:185–196CrossRef
12.
Zhao Y, Sun S, Han Z, Li Q (2011) Positive solutions to boundary value problems of nonlinear fractional differential equations. Abstr Appl Anal 2011:1–16MathSciNet
13.
Wilhelm HE (1970) Analysis of nonlinear initial-boundary-value problems in recombination and diffusion kinetics. J Chem Phys 53:1677MathSciNetCrossRefMATH
14.
Rabeony HM, Reiss H (1989) Exact solution of boundary value problems involving linear processes of growth and transport in addition polymerization. J Chem Phys 91:1875CrossRef
15.
16.
Narayana M, Sibanda P, Motsa SS, Siddheshwar PG (2012) On double-diffusive convection and cross diffusion effects on a horizontal wavy surface in a porous medium. Bound Value Probl 2012:88MathSciNetCrossRefMATH
17.
Liu J, Xu W (2009) Positive solutions for some beam equation boundary value problems. Bound Value Probl 2009:1–9MathSciNetCrossRef
18.
Park J, Bai H, Jang TS (2013) A numerical approach to static deflection analysis of an infinite beam on a nonlinear elastic foundation: one-way spring model. J Appl Math 2013:1–10MathSciNetMATH
19.
Xie J, Luo Z (2013) Solutions to a boundary value problem of a fourth-order impulsive differential equation. Bound Value Probl 2013:154MathSciNetCrossRefMATH
20.
21.
22.
Raja MAZ (2014) Unsupervised neural networks for solving Troesch’s problem. Chin Phys B 23:018903CrossRef
23.
Raja MAZ (2014) Stochastic numerical techniques for solving Troesch’s problem. Inf Sci 279:860–873CrossRefMATH
24.
Raja MAZ (2014) Solution of one-dimensional Bratu equation arising in the fuel ignition model using ANN optimized with PSO and SQP. Connect Sci 26:195–214CrossRef
25.
Raja MAZ, Ahmad S-U-I, Samar R (2014) Solution of the 2-dimensional Bratu problem using neural network, swarm intelligence and sequential quadratic programming. Neural Comput Appl 25:1723–1739. doi:10.​1007/​s00521-014-1664-3 CrossRef
26.
Raja MAZ, Samar R (2014) Numerical treatment for nonlinear MHD Jeffery–Hamel problem using neutral network optimized with interior point algorithm. Neurocomputing 124:178–193CrossRef
27.
Raja MAZ (2014) Numerical treatment for boundary value problems of pantograph functional differential equation using computational intelligence algorithms. Appl Soft Comput 24:806–821CrossRef
28.
Raja MAZ (2014) Numerical treatment of nonlinear MHD Jeffery–Hamel problems using stochastic algorithms. Comput Fluids 91:28–46MathSciNetCrossRef
29.
Raja MAZ, Khan JA, Behloul D, Haroon T, Siddiqui AM, Samar R (2015) Exactly satisfying initial conditions neural network models for numerical treatment of first Painleve equation. Appl Soft Comput 26:244–256CrossRef
30.
Yazdi HS, Pourreza R (2010) Unsupervised adaptive neural-fuzzy inference system for solving differential equations. Appl Soft Comput 10:267–275CrossRef
31.
Shirvany Y, Hayati M, Moradian R (2009) Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations. Appl Soft Comput 9:20–29CrossRef
32.
Sobester A, Nair PB, Keane AJ (2008) Genetic programming approaches for solving elliptic partial differential equations. IEEE Trans Evol Comput 12:469–478CrossRef
33.
Tsoulos IG, Lagaris IE (2006) Solving differential equations with genetic programming. Genet Program Evolvable Mach 7:33–54CrossRef
34.
Zameer A, Mirza SM, Mirza NM (2013) Core loading pattern optimization of a typical two-loop 300 MWe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) ahd hybrid GA (SA) schemes. Ann Nucl Energy 65:122–131CrossRef
35.
Abo-Hammour ZS, Arqub OA, Alsmadi O, Momani S, Alsaedi A (2014) An optimization algorithm for solving systems of singular boundary value problems. Appl Math Inf Sci 8:2809–2821MathSciNetCrossRef
36.
Abo-Hammour ZS, Arqub OA, Momani S, Shawagfeh N (2014) Optimization solution of Troesch’s and Bratu’s problems of ordinary type using novel continuous genetic algorithm. Discrete Dyn Nat Soc. doi:10.​1155/​2014/​401696 MathSciNetMATH
37.
Arqub OA, Abo-Hammour ZS (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415MathSciNetCrossRefMATH
38.
Arqub OA, Abo-Hammour ZS, Momani S (2014) Application of continuous genetic algorithm for nonlinear system of second-order boundary value problems. Appl Math Inf Sci 8:235–248CrossRef
39.
40.
41.
Kashan MH, Kashan AH, Nahavandi N (2013) A novel differential evolution algorithm for binary optimization. Comput Optim Appl 55:481–513MathSciNetCrossRefMATH
42.
Ali MM, Zhu WX (2013) A penalty function-based differential evolution algorithm for constrained global optimization. Comput Optim Appl 54:707–739MathSciNetCrossRefMATH
43.
Storn R, Price K (1997) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. J Glob Optim 11:341–359CrossRefMATH
44.
Abo-Hammour ZS, Yusuf M, Mirza NM, Mirza SM, Arif M, Khurshid J (2004) Numerical solution of second-order, two-point boundary value problems using continuous genetic algorithms. Int J Numer Methods Eng 61:1219–1242CrossRefMATH
45.
Yang M, Cai Z, Li C, Guan J (2013) An improved adaptive differential evolution algorithm with population adaptation. Genet Evol Comput Conf, Copyright 2013 ACM 978-1-4503-1963-8/13/07, Amsterdam, pp 145–152
46.
Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33:61–106CrossRef
47.
Tat CK, Majid ZA, Suleiman M, Senu N (2012) Solving linear two-point boundary value problems by Direct Adams Moulton method. Appl Math Sci 6:4921–4929MathSciNetMATH
Metadaten
Titel
Biologically inspired computing framework for solving two-point boundary value problems using differential evolution
verfasst von
Muhammad Faisal Fateh
Aneela Zameer
Nasir M. Mirza
Sikander M. Mirza
Muhammad Asif Zahoor Raja
Publikationsdatum
19.01.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2185-z

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