Optimization Solution of Troesch’s and Bratu’s Problems of Ordinary Type Using Novel Continuous Genetic Algorithm

Abstract

A new kind of optimization technique, namely, continuous genetic algorithm, is presented in this paper for numerically approximating the solutions of Troesch’s and Bratu’s problems. The underlying idea of the method is to convert the two differential problems into discrete versions by replacing each of the second derivatives by an appropriate difference quotient approximation. The new method has the following characteristics. First, it should not resort to more advanced mathematical tools; that is, the algorithm should be simple to understand and implement and should be thus easily accepted in the mathematical and physical application fields. Second, the algorithm is of global nature in terms of the solutions obtained as well as its ability to solve other mathematical and physical problems. Third, the proposed methodology has an implicit parallel nature which points to its implementation on parallel machines. The algorithm is tested on different versions of Troesch’s and Bratu’s problems. Experimental results show that the proposed algorithm is effective, straightforward, and simple.

A Numerical Iterative Method for Solving Systems of First-Order Periodic Boundary Value Problems

“…But on the other hand, from Theorem 10, , ( ) converge uniformly to ( ). It follows that, on taking limits in (17),…”

“…, then the -term approximate solutions , ( ) in the iterative formula (17) converge to the exact solutions ( ) of (9) in the space 2 2 [0, 1] and ( ) = ∑ ∞ =1 { } ( ), = 1, 2, . .…”

“…Recently, the existence of positive solutions for systems of first-order periodic BVPs is proposed in [10]. For more results on the solvability analysis of solutions for systems of firstorder periodic BVPs, we refer the reader to [11][12][13][14][15], and for numerical solvability of different categories of BVPs, one can consult [16][17][18][19]. 2…”

Neural network methods to solve the Lane–Emden type equations arising in thermodynamic studies of the spherical gas cloud model

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Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks

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