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# Applying particle swarm optimization based on Padé approximant to solve ordinary differential equation

• * Corresponding author: Azzam S. Y. Aladool
• Ordinary differential equations are converted into a constrained optimization problems to find their approximate solutions. In this work, an algorithm is proposed by applying particle swarm optimization (PSO) to find an approximate solution of ODEs based on an expansion approximation. Since many cases of linear and nonlinear ODEs have singularity point, Padé approximant which is fractional expansion is employed for more accurate results compare to Fourier and Taylor expansions. The fitness function is obtained by adding the discrete least square weighted function to a penalty function. The proposed algorithm is applied to 13 famous ODEs such as Lane Emden, Emden-Fowler, Riccati, Ivey, Abel, Thomas Fermi, Bernoulli, Bratu, Van der pol, the Troesch problem and other cases. The proposed algorithm offer fast and accurate results compare to the other methods presented in this paper. The results demonstrate the ability of proposed approach to solve linear and nonlinear ODEs with initial or boundary conditions.

Mathematics Subject Classification: Primary: 34Bxx, 65Lxx, 65Kxx, 41A21; .

 Citation: • • Figure 1.  shows a plot of the resulting approximate solution with the corresponding exact solutions.

Figure 2.  Shows numerical error between the approximate solution and exact solution arising from the proposed algorithm in four examples.in NLIVP5, NLIVP6, NBVP1 and NBVP3 respectively.

Figure 3.  Show the convergence of PSO-PEA-DLSWF algorithm in 500 iteration.

Figure 4.  Comparison of expansions: PEA, TEA and FEA to consumed time.

Figure 5.  shows the simulating of example (NLBVP3) in terms of errors, convergence and stability.

Figure 6.  Comparison between the fitness function: WRF, LSWF and DLSWF with consumed time.

Figure 7.  shows the simulating of example (NLBVP1) in terms of errors, convergence and stability.

Table 1.  Illustrates different type of the ordinary differential equations, with its conditions, domain and exact solution.

 ODE NAME ODE EQUATION I.C. or B.C DOMAIN EXACT SOLUTION Simple ODEs1 LIVP1 $y'=\frac{y}{x}+1$ $y(1)=0$ $x\in[1,2]$ $y=xln(x)$ Simple ODEs2 LIVP2 $y''-y=e^x$ $y(0)=0$ $y'(0)=1$ $x\in[0,1]$ $y=0.25(e^x+2xe^x-e^{-x})$ Simple Harmonic LBVP1 $y''+25y=0$ $y(0)=1$ $y(\frac{\pi}{10})=0$ $x\in[0,\frac{\pi}{10}]$ $y=\cos(5x)$ Riccati NLIVP1 $y'=1+2y-y^2$ $y(0)=0$ $x\in[0,1]$ $y=1+\sqrt{2} \tanh{(\sqrt{2}x}+\frac{1}{2} ln(\frac{\sqrt{2}-1}{\sqrt{2}+1}))$ Abel NLIVP2 $y'=1-x^2 y+y^3$ $y(0)=0$ $x\in[0,1]$ $y=x$ Ivey NLIVP3 $y''=\frac{(y' )^2}{y}-\frac{2y'}{x}-2y^2$ $y(1)=1$ $y'(1)=-2$ $x\in[1,2]$ $y=\frac{1}{x^2}$ Lane- Emden NLIVP4 $y''+2\frac{y'}{x}=-4(2e^y+e^{\frac{y}{2}})$ $y(0)=0$ $y'(0)=0$ $x\in[0,1]$ $y=-2ln(1+x^2)$ Emden-Fowler NLIVP5 $y''+6\frac{y'}{x}+14y+4ylny=0$ $y(0)=1$ $y'(0)=0$ $x\in[0,1]$ $y=e^{-x^2}$ Van Der Pol NLIVP6 $y''+y=0.05(1-y^2 )y'$ $y(0)=0$ $y'(0)=0.5$ $x\in[0,1]$ Sol. By ode45 solver in MATLAB Bratu-Gelfand NLBVP1 $y''-\pi^2 e^y=0$ $y(0)=0$ $y(1)=0$ $x\in[0,1]$ $y=-2ln(\sqrt{2}\cos(\pi(\frac{x}{2}-\frac{1}{4})))$ Thomas-Fermi NLBVP2 $y''=\sqrt{\frac{y^3}{x}}$ $y(1)=144$ $y(2)=18$ $x\in[1,2]$ $y=\frac{144}{x^3}$ Bernoulli NLBVP3 $y''+(y')^2=2e^{-y}$ $y(0)=0$ $y(1)=0$ $x\in[0,1]$ $y=\log{(0.75+(x-0.5)^2)}$ Troesch Problem NLBVP4 $y''=\sinh{y}$ $y(0)=0$ $y(1)=1$ $x\in[0,1]$ Sol. By bvp4c solver in MATLAB

Table 2.  the values of parameters and inputs used in all example.

 parameter $\phi_1$ $\phi_2$ $\kappa$ $\rho$ $\sigma$ $h$ $TOL$ $K_m \forall m$ $Maxit$ $nPop$ value 2.05 2.05 1 0.8 0.4 0.01 1e-03 1 or 10 500 200

Table 3.  Search space and nVar sitting of each example.

 Equation PEA TEA FEA VarMin VarMax nVar VarMin VarMax nVar VarMin VarMax nVar LIVP1 -1 1 10 -2 2 10 -2 2 9 LIVP2 -2 2 10 -2 2 10 -2 2 11 LBVP1 -3 3 10 -3 3 10 -1 1 11 NLIVP1 -2 2 10 -2 2 10 -2 2 9 NLIVP2 0 1 10 0 1 10 -1 1 11 NLIVP3 0 1 10 -3 3 10 -2 2 9 NLIVP4 -2 2 10 -2 2 10 -1 1 11 NLIVP5 -1 1 10 -1 1 10 -1 1 9 NLIVP6 -1 1 10 -2 2 10 -0.5 0.5 9 NLBVP1 -1 1 10 -3 3 10 -1 1 9 NLBVP2 0 1 10 -900 900 10 -200 200 9 NLBVP3 -2 2 10 -3 3 10 -1 1 9 NLBVP4 -4 4 10 -1 1 10 -1 1 9

Table 4.  shows the values of variables founded by using the PSO-PEA-DLSWF algorithm.

 Eq. Coff, $m=1$ $m=2$ $m=3$ $m=4$ $m=5$ LIVP1 $\alpha_m$ 0.449646621 -0.955885566 -0.662451518 0.5990158869 0.5696745764 $\beta_m$ -0.383029549 1.000000000 0.9928258613 0.2090703809 -0.01223876 LIVP2 $\alpha_m$ 1.5905e-08 -2.000000000 -1.99993129 -1.999993802 1.320889355 $\beta_m$ -2.000000000 -1.028027457 -0.640309044 1.893006788 -0.627961135 LBVP1 $\alpha_m$ -0.23700822 0.2939052397 1.7497711122 -0.815400817 -0.28105974 $\beta_m$ -0.237008298 0.2940587929 -1.172283084 1.983810204 -2.525130497 NLIVP1 $\alpha_m$ -6.595e-07 -2.000000000 -1.969510526 -2.000000000 -1.99980317 $\beta_m$ -1.999999998 0.1334264437 -1.999346637 0.2828697917 -1.132644395 NLIVP2 $\alpha_m$ 0.00000000 1.00000000 1.00000000 0.9159523195 1.00000000 $\beta_m$ 1.00000000 1.00000000 0.9159523195 1.00000000 0.00000000 NLIVP3 $\alpha_m$ 1.00000000 0.501789937 0.3963523812 0.00000000 0.00000000 $\beta_m$ 0.00000000 0.00000000 1.00000000 0.501789937 0.3963523812 NLIVP4 $\alpha_m$ -3.556e-10 1.1605e-09 1.371544377 1.705016485 1.664659425 $\beta_m$ -0.699013232 -0.795001437 -1.237894199 -0.487132482 -0.20116878 NLIVP5 $\alpha_m$ -1.0000000 -0.999999714 0.9996994842 1.00000000 -0.670316746 $\beta_m$ -0.999999761 -1.0000000 0.0694451202 -0.363172581 0.4710444283 NLIVP6 $\alpha_m$ 9.8817e-08 0.499999967 -0.128335779 -0.225730262 0.4574883022 $\beta_m$ 1.00000000 -0.334983343 0.0548712929 0.1919886974 0.4900184664 NLBVP1 $\alpha_m$ 6.0988e-07 -1.0000000 -0.168001861 0.1680024128 1.00000000 $\beta_m$ 0.3182717396 0.5556535208 0.2683350684 0.1086186654 -0.242687286 NLBVP2 $\alpha_m$ 1.00000000 0.00000000 0.00000000 0.00000000 0.00000000 $\beta_m$ 0.00000000 0.00000000 0.00000000 0.0069444444 0.00000000 NLBVP3 $\alpha_m$ -2.979e-11 -1.512033065 0.5287945264 -0.376563712 1.359802251 $\beta_m$ 1.512152004 0.2390046775 1.385502167 0.2230003398 0.4953153062 NLBVP4 $\alpha_m$ 2.6966e-06 -3.398796238 -0.916386583 -3.997792287 -2.079546692 $\beta_m$ -4.00000000 -1.420908971 -2.843010022 -3.999816103 1.8712207261

Table 5.  Comparison of expansions: PEA, TEA and FEA. The capital letters P, T and F denotes when PEA, TEA and FEA is the best results respectively.

 Equation PEA TEA FEA No. It RMSE Time (s) No. It RMSE Time (s) No. It RMSE Time (s) LIVP1 047 6.98e-04 26.79 113 9.70e-04 37.70 349 3.28e-02 090.10 P LIVP2 017 3.35e-04 10.37 062 8.50e-04 21.39 500 4.68e-02 130.30 P LBVP1 019 6.49e-04 09.25 081 9.87e-04 21.64 128 9.69e-04 027.38 P NLIVP1 049 9.53e-04 25.92 122 9.90e-04 39.58 500 1.50e-02 093.13 P NLIVP2 006 4.95e-04 03.61 003 0.00e-00 02.06 500 2.90e-02 193.67 T NLIVP3 006 6.83e-04 09.67 500 1.36e-03 247.7 500 9.90e-03 233.29 P NLIVP4 028 9.80e-04 28.07 500 8.60e-03 189.1 500 6.00e-02 181.89 P NLIVP5 008 6.70e-04 7.122 500 2.60e-03 238.3 500 2.30e-02 201.20 P NLIVP6 014 7.95e-04 18.14 040 9.90e-04 36.58 500 9.10e-03 339.87 P NLBVP1 014 9.01e-04 08.89 500 2.10e-02 151.0 500 4.80e-03 152.02 P NLBVP2 013 8.69e-04 12.08 500 2.14e-01 143.2 500 6.30e-01 126.00 P NLBVP3 014 8.59e-04 16.40 500 5.80e-03 158.5 500 1.49e-03 226.66 P NLBVP4 013 4.58e-04 10.62 500 2.30e-03 200.7 500 2.15e-03 121.30 P

Table 6.  Comparison of fitness function: WRF, LSWF and DLSWF. W, L and D symbols indicates that method PEA-PSO-WRF, PEA-PSO-LSWF and PEA-PSO-DLSWF is the best results respectively.

 Equation WRF LSWF DLSWF No. It RMSE Time (s) No. It RMSE Time (s) No. It RMSE Time (s) LIVP1 012 8.03e-04 14.13 014 5.91e-03 22.88 039 1.24e-03 58.56 W LIVP2 012 8.98e-04 15.68 016 9.57e-04 29.54 030 6.09e-04 22.33 W LBVP1 031 6.76e-04 23.92 027 9.14e-04 25.76 018 9.81e-04 11.42 D NLIVP1 212 8.38e-04 270.9 150 9.94e-04 182.0 057 9.51e-04 77.09 D NLIVP2 004 1.42e-17 07.64 003 1.44e-17 04.26 003 0.00e-00 07.48 L NLIVP3 003 3.79e-17 08.43 003 3.79e-17 08.77 003 3.79e-17 07.71 D NLIVP4 077 9.81e-04 172.9 246 8.19e-04 486.1 016 8.96e-04 39.77 D NLIVP5 013 7.60e-04 40.40 018 8.52e-04 36.23 009 6.52e-04 25.20 D NLIVP6 027 7.14e-04 85.23 018 7.86e-04 39.10 012 9.79e-04 23.62 D NLBVP1 034 6.13e-04 54.26 074 9.14e-04 114.2 018 8.97e-04 33.88 D NLBVP2 019 1.86e-04 35.08 027 7.55e-05 48.71 012 2.52e-04 17.02 D NLBVP3 016 3.75e-04 27.01 011 8.91e-04 18.40 018 9.11e-04 28.41 L NLBVP4 015 8.11e-04 25.80 016 7.19e-04 22.62 021 8.84e-04 24.08 L
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Tables(6)

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