Research article

Adaptation of species as response to climate change: Predator-prey mathematical model

  • Most of the species currently threatened with extinction seem to be under the pressure of unsuitable environmental conditions; e.g., climate change, scarce food resource, habitat fragmentation. One should expect species to have forms of resilience against such extinction. The point here is to examine the effect of spatial gradients on species survival against increasing temperature arising from climate change. Therefore, we start with the question of whether, when faced with extinction stemming from climate change, a spatial gradient and a beachhead have the power to prevent extinction. This problem is addressed theoretically using a coupled reaction diffusion equation for a predator-prey system in which the prey experiences an Allee effect. It is demonstrated that there exists a relationship between the slope of the gradient and the beachhead at which the predator-prey system can stably survive. The tendency of the system can be defined by a function where the system includes the threshold point for extinction, that separates the areas of extinction and survival. The findings reveal that spatial gradient can be used as a precaution, when the species faces to extinction, for species to create new habitat and sustain its persistence. Therefore, in this paper, it is shown that, in theory, the recovery of species from unsuitable environmental conditions can be achieved. This can be possible by taking into account the spatial gradient to slow down the forthcoming ecological extinction, and thus extend the system a while as an adaptation mechanism.

    Citation: Yadigar Sekerci. Adaptation of species as response to climate change: Predator-prey mathematical model[J]. AIMS Mathematics, 2020, 5(4): 3875-3898. doi: 10.3934/math.2020251

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  • Most of the species currently threatened with extinction seem to be under the pressure of unsuitable environmental conditions; e.g., climate change, scarce food resource, habitat fragmentation. One should expect species to have forms of resilience against such extinction. The point here is to examine the effect of spatial gradients on species survival against increasing temperature arising from climate change. Therefore, we start with the question of whether, when faced with extinction stemming from climate change, a spatial gradient and a beachhead have the power to prevent extinction. This problem is addressed theoretically using a coupled reaction diffusion equation for a predator-prey system in which the prey experiences an Allee effect. It is demonstrated that there exists a relationship between the slope of the gradient and the beachhead at which the predator-prey system can stably survive. The tendency of the system can be defined by a function where the system includes the threshold point for extinction, that separates the areas of extinction and survival. The findings reveal that spatial gradient can be used as a precaution, when the species faces to extinction, for species to create new habitat and sustain its persistence. Therefore, in this paper, it is shown that, in theory, the recovery of species from unsuitable environmental conditions can be achieved. This can be possible by taking into account the spatial gradient to slow down the forthcoming ecological extinction, and thus extend the system a while as an adaptation mechanism.


    In recent decades, importance of fractional order models is well disclosed fact in many fields of engineering and science. Numerous fractional order partial differential equations(FPDEs) have been used by many authors to describe various important biological and physical processes like in the fields of chemistry, biology, mechanics, polymer, economics, biophysics control theory and aerodynamics. In this concern, many researchers have studied various schemes and aspects of PDEs and FPDEs as well, see [1,2,3,4,5,6,7,8,9,10]. However, the great attention has been given very recently to obtaining the solution of fractional models of the physical interest. Keeping in views, the computation complexities involved in fractional order models is very crucial and is the difficulty in solving these fractional models. Some times, the exact analytic solution of each and every FPDE can not be obtained using the traditional schemes and methods. However, there exists some schemes and methods, which have been proved to be efficient in obtaining the approximation to solution of the fractional order problems. Among them, we bring the attention of readers to these methods and schemes [11,12,13,14,15,16,17,18,19,20,21] which are used successfully. These methods and schemes have their own demerits and merits. Some of them provide a very good approximation with convenient way. For example, see the methods and schemes in the articles [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39].

    The main aim of this work is to develop a new procedure which is easy with respect to application and more efficient as compare with existing procedures. In this concern, we introduced asymptotic homotopy perturbation method (AHPM) to obtain the solution of nonlinear fractional order models. It is a new version of perturbation techniques. In simulation section, we have testified our proposed procedure by considering the test problems of non linear fractional order Zakharov-Kuznetsov ZK(m,n,r) equations of the form [11,12]

    αu(x,y,t)tα+a0(um(x,y,t))x+a1(un(x,y,t))xxx+a2(ur(x,y,t))yyx=0,0<α1. (1.1)

    Where a0, a1, a2 are arbitrary constants and m,n,r are non zero integers. If α=1, then equation (1.1) becomes classical Zakharov-Kuznetsov ZK(m,n,r) equation given as:

    u(x,y,t)t+a0(um(x,y,t))x+a1(um(x,y,t))xxx+a2(ur(x,y,t))yyx=0. (1.2)

    The ZK equation has been firstly modelized for depicting weakly nonlinear ion-acoustic waves in strongly magnetized lossless plasma [40]. The ZK equation governs the behavior of weakly nonlinear ion-acoustic waves in plasma comprising cold ions and hot isothermal electrons in the presence of a uniform magnetic field [41,42].

    The plan of the rest paper is as follows: Section 2 provides theory of the AHPM; Section 3 provides implementation of AHPM. Finally, a brief conclusion and the further work has been listed.

    Here, we provide that the Caputo type fractional order derivative will be used throughout this paper for the computation of derivative.

    Let us consider the nonlinear problem in the form as

    T(u(x,y,t))+g(x,y,t)=0, (2.1)
    B(u(x,y,t),u(x,y,t)t)=0. (2.2)

    Where T(u(x,y,t)) denotes a differential operator which may consists ordinary, partial or space- fractional or time-fractional differential derivative. T(u(x,y,t)) can be expressed for fractional model as follows:

    αu(x,y,t)tα+N(u(x,y,t))+g(x,y,t)=0 (2.3)

    subject to the condition

    B(u(x,y,t),u(x,y,t)t)=0, (2.4)

    where the operator αtα denotes the Caputo derivative operator, N is non linear operator and B denotes a boundary operator, u(x,y,t) is unknown exact solution of Eq. (2.1), g(x,y,t) denotes known function and x,y and t denote special and temporal variables respectively. Let us construct a homotopy Φ(x,y,t;p):Ω×[0,1]R which satisfies

    αΦ(x,y,t;p)tα+g(x,y,t)p[N(Φ(x,y,t;p)]=0, (2.5)

    where p[0,1] is said to be an embedding parameter. At this phase of our work it is pertinent that our proposed deformation Eq. (2.5) is an alternate form of the deformation equations as

    (1p)[L(Φ(x,y,t;p))L(u0(x,y,t))+g(x,y,t)]+p[T(Φ(x,y,t;p)+g(x,y,t)]=0, (2.6)
    (1p)[L(Φ(x,y,t;p))L(u0(x,y,t))]=hp[T(Φ(x,y,t;p)+g(x,y,t)] (2.7)

    and

    (1p)[L(Φ(x,y,t;p))+g(x,y,t)]H(p)[T(Φ(x,y,t;p)+g(x,y,t)]=0. (2.8)

    in HPM, HAM, OHAM proposed by Liao in [43], He in [44] and Marinca in [45] respectively.

    Basically, according to homotopy definition, when p=0 and p=1 we have

    Φ(x,y,t;p)=u0(x,y,t),ϕ(x,y,t;p)=u(x,y,t).

    Obviously, when the embedding parameter p varies from 0 to 1, the defined homotopy ensures the convergence of ϕ(x,y,t;p) to the exact solution u(x,y,t). Consider ϕ(x,y,t;p) in the form

    Φ(x,y,t;p)=u0(x,y,t)+i=1ui(x,y,t)pi (2.9)

    and assuming N(Φ(x,y,t;p)) as follows

    N(Φ(x,y,t;p))=B1N0+i=1(im=0Bi+1mNm)pi,B1+B2+B3+...=1. (2.10)

    Where

    Bi=Bi(x,y,t,ci),for,i=1,2,3,. (2.11)

    are arbitrary auxiliary functions, will be discussed later. Thus, if p=0 and p=1 in Eq. (2.5), we have

    αu(x,y,t)tα+f(x)=0, and αu(x,y,t)tα+N(u(x,y,t))+g(x,y,t)=0,

    respectively.

    It is obvious that the construction of introduced auxiliary function in Eq. (2.10) is different from the auxiliary functions that are proposed in articles [43,44,45]. Hence the procedure proposed in our paper is different from the procedures proposed by Liao, He, Marinca in aforesaid papers [43,44,45] as well as Optimal Homotopy perturbation method (OHPM) in [46].

    Furthermore, when we substitute Eq. (2.9) and Eq. (2.10) in Eq. (2.5) and equate like power of p, the obtained series of simpler linear problems are

    p0:αu0(x,y,t)tα+g=0,p1:αu1(x,y,t)tα=B1N0,p2:αu2(x,y,t)tα=B2N0+B1N1,p3:αu3(x,y,t)tα=B3N0+B2N1+B1N2,pk:αuk(x,y,t)tα=k1i=0BkiNi.

    We obtain the series solutions by using the integral operator Jα on both sides of the above each simple fractional differential equation. The convergence of the series solution Eq. (2.9) to the exact solution depends upon the auxiliary parameters (functions) Bi(x,y,t,ci). The choice of Bi(x,y,t,ci) is purely on the basis of terms appear in nonlinear part of the Eq. (2.1). The Eq.(2.9) converges to the exact solution of Eq. (2.1) at p=1:

    ˜u(x,y,t)=u0(x,y,t)+k=1uk(x,y,t;ci), i=1,2,3,. (2.12)

    Particularly, we can truncate the Eq. (2.12) into finite m-terms to obtain the solution of nonlinear problem. The auxiliary convergence control constants c1,c2,c3, can be found by solving the system

    R(x1,y1)=R(x2,y2)=R(x3,,y3)==R(xm,,ym)=0,xi,yi[a,b]. (2.13)

    It can be verified to observe that HPM is only a case of Eq. (2.5) when p=p and

    N(Φ(x,y,t;p))=N0+i=1Nipi.

    The HAM is also a case of Eq. (2.5) when p=ph and

    N(Φ(x,y,t;p))=N0+i=1Nipi.

    The OHAM is also another case when

    Bk1=Bk2+hk(t,cj)+k2i=0h(k(i+1))(t,cj)Bi, and hk(t,cj)=ck

    in Eq. (2.10), we obtain exactly the series problems which are obtained by OHAM after expanding and equating the like power of p in deformation equation. Furthermore, concerning the Optimal Homotopy Asymptotic Method (OHAM) mentioned in this manuscript and presented in [45], that the version of OHAM proposed in 2008 was improved in time and the most recent improvement, which also contains an auxiliary functions, are presented in the papers [47,48]. We also have improved the version of OHAM by introducing a very new auxiliary function in Eq. (2.10). Our method proposed in this paper uses a very new and more general form of auxiliary function

    N(ϕ(x,y,t;p))=B1N0+i=1(im=0Bi+1mNm)pi

    which depends on arbitrary parameters B1,B2,B3, and is useful for adjusting and controlling the convergence of nonlinear part as well as linear part of the problem with simple way.

    In this portion, we apply AHPM to obtain solution of the following problems to show the accuracy and appropriateness of the new procedure for to solve nonlinear problems.

    Problem 3.1. Let us consider FZK(2,2,2) in the form:

    αu(x,y,t)tα+(u2(x,y,t))x+18(u2(x,y,t))xxx+18(u2(x,y,t))yyx=0,0<α1, (3.1)

    subject to the condition

    u(x,y,0)=43ksinh2(x+y).

    When α=1. Then the exact solution of Eq. (3.1):

    u(x,y,t)=43ksinh2(x+ykt).

    As in Eq. (3.1), the non linear part

    N(u(x,y,t))=(u2(x,y,t))x+18(u2(x,y,t))xxx+18(u2(x,y,t))yyx.

    Now, follow the procedure of AHPM, we obtain series of the simpler linear problems as:

    Zero order problem:

    αu0tα=0,u0=43ksinh2(x+y). (3.2)

    First order problem:

    αu1tα=B1N0,u1=0. (3.3)

    Second order problem:

    αu2tα=B2N0+B1N1,u2=0. (3.4)

    Third order problem:

    αu3tα=B3N0+B2N1+B1N2,u3=0. (3.5)

    Fourth order problem:

    αu4tα=B4N0+B3N1+B2N2+B1N3,u4=0 (3.6)

    and so on.

    Solving the above equations, the respective solutions from Eqs. (3.2)–(3.6) are given as follow:

    u0(x,y,t)=43ksinh2(z) (3.7)
    u1(x,y,t)=B1N0tαΓ(α+1) (3.8)
    u2(x,y,t)=B2N0tαΓ(α+1)+6427B12k3[13cosh(2z)70cosh(4z)+75cosh(6z)]t2αΓ(2α+1), (3.9)
    u3(x,y,t)=B3N0tαΓ(α+1)+6427B1B2k3[13cosh(2z)70cosh(4z)+75cosh(6z)]t2αΓ(2α+1)+B16481k3[B2(39cosh(2z)210cosh(4z)+225cosh(6z))t2αΓ(2α+1)+B12k(160sinh(2z)+320sinh(4z)2400sinh(6z)+3400sinh(8z))Γ(2α+1)t2α(Γ(α+1))2Γ(3α+1)+B12k(768sinh(2z)+9120sinh(4z)26400sinh(6z)+20400sinh(8z))t3αΓ(3α+1)] (3.10)
    u4(x,y,t)=B4N0tαΓ(α+1)+6427B1B3k3[13cosh(2z)70cosh(4z)+75cosh(6z)]t2αΓ(2α+1)+B26481k3[B2(39cosh(2z)210cosh(4z)+225cosh(6z))t2αΓ(2α+1)+B12k(160sinh(2z)+320sinh(4z)2400sinh(6z)+3400sinh(8z))Γ(2α+1)t2α(Γ(α+1))2Γ(3α+1)+B12k(768sinh(2z)+9120sinh(4z)26400sinh(6z)+20400sinh(8z))t3αΓ(3α+1)]+B164243k3[B13k2(1022400cosh(6z)15200cosh(4z)2502400cosh(8z)+1768000cosh(10z))2Γ(2α)t4αα(Γ(α))2Γ(4α+1)+B13k2(85248cosh(2z)1816320cosh(4z)+9878400cosh(6z)18278400cosh(8z)+10608000cosh(10z))t4αΓ(4α+1)+B3(117cosh(2z)630cosh(4z)+675cosh(6z))t2αΓ(2α+1)+B13k2(56640cosh(2z)+119040cosh(4z)+496800cosh(6z)2121600cosh(8z)+2340000cosh(10z))Γ(3α+1)t4αΓ(α+1)Γ(2α+1)Γ(4α+1)+B1B2k(1440sinh(2z)+2880sinh(4z)21600sinh(6z)+30600sinh(8z))Γ(2α+1)t3α(Γ(α+1))2Γ(3α+1)+B1B2k(4608sinh(2z)+54720sinh(4z)158400sinh(6z)+122400sinh(8z))t3αΓ(3α+1)] (3.11)

    so on .

    In similar way, we can compute the solution of the next simpler linear problems which are difficult to compute by using OHAM procedure. we choose B1=c1,B2=c2,B3=c3,B4=c4 and consider

    ˜u(x)=u0(x)+u1(x,c1)+u2(x,c1,c2)+u3(x,c1,c2,c3)+u4(x,c1,c2,c3,c4). (3.12)

    The residual:

    R(˜u(x,y,t))=α˜u(x,y,t)tα+(˜u2(x,y,t))x+18(˜u2(x,y,t))xxx+18(˜u2(x,y,t))yyx. (3.13)

    We obtain number of optimal values of auxiliary constants by using the Eq. (2.13) and choose those optimal values whose sum is in [1,0). Now, substituting the optimal values of auxiliary constants (from the Table 1) into the Eq. (3.12), we obtain the AHPM solutions for different values of α at k=0.001

    Table 1.  The auxiliary control constants for the problem 3.1.
    Aux.Const. α=1 α=0.75 α=0.67
    c1 0.03206298594 0.02857059949 0.02811316381
    c2 0.05626044816 0.09201640919 0.09255449827
    c3 0.03255192821 0.23864503710 0.24163106460
    c4 0.09230916402 0.58155364530 0.59008434920
    c1+c2+c3+c4 0.0286 0.4064 0.4129

     | Show Table
    DownLoad: CSV

    If α=1, then we have

    ˜u(x,y,t)=6.67e4cosh(2z)1.5e36cosh(2x2y)+1.52e10t2cosh(2z)8.2e10t2cosh(4z)9.82e19t4cosh(2z)1.96e17t4cosh(4z)3.44e16t4cosh(8z)+9.74e15t3sinh(2z)2.7e13t3sinh(4z)7.91e13t3sinh(8z)+2.45e16t4cosh(10z)+8.79e10t2cosh(6z)+1.56e16t4cosh(6z)+8.85e13t3sinh(6z)+1.02e7tsinh(2z)1.27e7tsinh(4z)6.67e4.

    If α=0.75, then we have

    ˜u(x,y,t)=6.67e4cosh(2z)1.5e36cosh(2z)9.63e19t3cosh(2z)5.12e17t3cosh(4z)+4.09e10t3/2cosh(2z)7.79e16t3cosh(8z)2.2e9t3/2cosh(4z)+1.57e6t3/4sinh(2z)1.97e6t3/4sinh(4z)+2.65e14t9/4sinh(2z)6.14e13t9/4sinh(4z)1.74e12t9/4sinh(8z)+5.34e16t3cosh(10z)+3.66e16t3cosh(6z)+2.36e9t3/2cosh(6z)+1.98e12t9/4sinh(6z)6.67e4.

    If α=0.67, then we have

    ˜u(x,y,t)=6.67e4cosh(2z)1.5e36cosh(2x2y)8.33e19t67/25cosh(2z)7.12e17t67/25cosh(4z)1.05e15t67/25cosh(8z)+4.57e10t67/50cosh(2z)2.46e9t67/50cosh(4z)+1.63e6t67/100sinh(2z)2.03e6t67/100sinh(4z)+3.45e14t201/100sinh(2z)7.54e13t201/100sinh(4z)2.1e12t201/100sinh(8z)+7.09e16t67/25cosh(10z)+4.97e16t67/25cosh(6z)+2.64e9t67/50cosh(6z)+2.41e12t201/100sinh(6z)6.67e4.

    Tables 2 and 3 show the AHPM solution, VIM solution, exact solution and absolute error of AHPM solution. It is obvious from Tables 2 and 3 that AHPM solution results are more accurate to the exact solution results as compare with VIM [11] solution results. The AHPM solution, exact solution and absolute error of AHPM solution are plotted for different values of α, x, y and t in Figures 1 and 2. The curves of AHPM and exact solution are exactly matching as compare with homotopy perturbation transform method (HPTM)[12]. It is obvious from the Tables 2 and 3, Figures 1 and 2, that the AHPM solution of the problem 3.1 is in very good agreement with exact solution.

    Table 2.  Solution of the problem 3.1 for various values of α, x, y and t at k=0.001.
    x y t VIM [1] (α=0.67) AHPM (α=0.67) VIM [1] (α=0.75) AHPM (α=0.75)
    0.1 0.1 0.2 5.312992862e5 0.000053661825095 5.325267164e5 0.000053719590053
    0.3 5.285029317e5 0.000053541299605 5.297615384e5 0.000053602860321
    0.4 5.260303851e5 0.000053433636044 5.272490734e5 0.000053495694982
    0.6 0.6 0.2 2.953543396e3 0.0029991909006 2.964363202e3 0.0030049612043
    0.3 2.928652795e3 0.0029871641418 2.939926307e3 0.0029932937681
    0.4 2.905913439e3 0.0029764476067 2.917239345e3 0.002982607386
    0.9 0.9 0.2 1.045289537e2 0.011096367296 1.064555345e2 0.011161869914
    0.3 0.990546789e2 0.010960230827 1.017186398e2 0.011029208413
    0.4 0.927982231e2 0.010839735959 0.960539982e2 0.010908463773

     | Show Table
    DownLoad: CSV
    Table 3.  Solution and absolute error of the problem 3.1 for various values of x, y and t at k=0.001,α=1.
    x y t VIM [1] AHPM Exact VIM [1] AHPM Error
    0.1 0.1 0.2 5.355612471e5 0.00005403406722 5.393877159e5 3.83e7 9.53e8
    0.3 5.331384269e5 0.000054026996643 5.388407669e5 5.7e7 1.43e7
    0.4 5.307396595e5 0.000054019939232 5.382941057e5 7.55e7 1.91e7
    0.6 0.6 0.2 2.991347666e3 0.0030365547595 3.036507411e3 4.52e5 4.73e8
    0.3 2.969760240e3 0.0030358658613 3.035778955e3 6.6e5 8.69e8
    0.4 2.948601126e3 0.0030351876618 3.035050641e3 8.64e5 1.37e7
    0.9 0.9 0.2 1.102746671e2 0.011526053047 1.153697757e2 5.1e4 1.09e5
    0.3 1.073227877e2 0.011518772848 1.153454074e2 8.02e4 1.58e5
    0.4 1.035600465e2 0.011511900292 1.153210438e2 0.00118 2.02e5

     | Show Table
    DownLoad: CSV
    Figure 1.  AHPM solution, exact solution and absolute error of AHPM solution of Problem 3.1 at α = 1 and t = 0.5 when k = 0.001.
    Figure 2.  AHPM solution of the Problem 3.1 for various values of x and y at t = 0.5 when k = 0.001.

    Problem 3.2. Now, we consider FZK(3,3,3) in the form:

    αu(x,y,t)tα+(u3(x,y,t))x+2(u3(x,y,t))xxx+2(u3(x,y,t))yyx=0,0<α1, (3.14)

    subject to condition

    u(x,y,0)=32ksinh(16(x+y)).

    When α=1. Then the exact solution of equation (3.14):

    u(x,y,t)=32ksinh(16(x+ykt)).

    As in Eq. (3.14), the non linear part:

    N(u(x,y,t))=(u3(x,y,t))x+2(u3(x,y,t))xxx+2(u3(x,y,t))yyx.

    Now, follow the procedure of AHPM, we obtain series of the simpler linear problems as follow:

    Zero order problem:

    αu0tα=0,u0=32ksinh(16(x+y)). (3.15)

    First order problem:

    αu1tα=B1N0,u1=0. (3.16)

    Second order problem:

    αu2tα=B2N0+B1N1,u2=0. (3.17)

    Third order problem:

    αu3tα=B3N0+B2N1+B1N2,u3=0. (3.18)

    Fourth order problem:

    αu4tα=B4N0+B3N1+B2N2+B1N3,u4=0. (3.19)

    The respective solutions of the Eqs. (3.15)–(3.19) are given as follow:

    u0(x,y,t)=32ksinh(16z),u1(x,y,t)=B1N0tαΓ(α+1),u2(x,y,t)=B2N0tαΓ(α+1)+332k5B12[801sinh3(16z)+765sinh4(16z)+127sinh(16z)]t2αΓ(2α+1),
    u3(x,y,t)=B3N0tαΓ(α+1)+332k5B1B2[801sinh3(16z)+765sinh4(16z)+127sinh(16z)]t2αΓ(2α+1)+B138192k5[B2(1120sinh(16z)9936sinh(12z)+12240sinh(56z))t2αΓ(2α+1)B12k2(1350cosh(12z)+2770cosh(16z)29070cosh(56z)+32886cosh(76z))Γ(2α+1)t3α(Γ(α+1))2Γ(3α+1)B12k2(56079cosh(12z)4155cosh(16z)182835cosh(56z)+155295cosh(76z))t3αΓ(3α+1)],
    u4(x,y,t)=B4N0tαΓ(α+1)+332k5B1B3[801sinh3(16z)+765sinh4(16z)+127sinh(16z)]t2αΓ(2α+1)+B238192k5[B2(1120sinh(16z)9936sinh(12z)+12240sinh(56z))t2αΓ(2α+1)B12k2(1350cosh(12z)+2770cosh(16z)29070cosh(56z)+32886cosh(76z))Γ(2α+1)t3α(Γ(α+1))2Γ(3α+1)B12k2(56079cosh(12z)4155cosh(16z)182835cosh(56z)+155295cosh(76z))t3αΓ(3α+1)]+B11131072k5[B13k3(937040cosh(13z)36000cosh(23z)+16083360cosh(43z))Γ(3α+1)t4αΓ(α+1)Γ(2α+1)Γ(4α+1)+B3(476928sinh(12z)+53760sinh(16z)+587520sinh(56z))t2αΓ(2α+1)+B13k4(552744sinh(12z)+1771470sinh(32z)63900sinh(16z)275400sinh(56z)1479870sinh(76z))Γ(3α+1)t4α(Γ(α+1))2Γ(4α+1)+B13k4(2349000sinh(12z)+39956490sinh(32z)21300sinh(16z)+23555880sinh(56z)57758778sinh(76z))Γ(2α+1)t4α(Γ(α+1))2Γ(4α+1)+B13k4(2948400sinh(12z)+33461100sinh(32z)540600sinh(16z)+9510480sinh(56z)39704364sinh(76z)10167120cosh(z))Γ(3α+1)t4αΓ(α+1)Γ(2α+1)Γ(4α+1)+B13k4(24230988sinh(12z)+188683425sinh(32z)+903510sinh(16z)+147146220sinh(56z)300495825sinh(76z))t4αΓ(4α+1)+B1B2k2(5383584cosh(12z)398880cosh(16z)17552160cosh(56z)+14908320cosh(76z))t3αΓ(3α+1)+B1B2k(1399680sinh(z)108160sinh(13z)576000sinh(23z))Γ(2α+1)t3α(Γ(α+1))2Γ(3α+1)+B1B2k2(129600cosh(12z)+265920cosh(16z)2790720cosh(56z)+3157056cosh(76z))Γ(2α+1)t3α(Γ(α+1))2Γ(3α+1)]

    and so on.

    In similar way, we can compute the solution of the next simpler linear problems. We choose B1=c1,B2=c2,B3=c3,B4=c4 and consider

    ˜u(x)=u0(x)+u1(x,c1)+u2(x,c1,c2)+u3(x,c1,c2,c3)+u3(x,c1,c2,c3,c4). (3.20)

    We compute the residual;

    R(˜u(x,y,t))=α˜u(x,y,t)tα+(˜u3(x,y,t))x+2(˜u3(x,y,t))xxx+2(˜u3(x,y,t))yyx.

    We obtain number of optimal values of auxiliary constants by using the Eq. (2.13) and choose those optimal values whose sum is in [1,0). Now, substituting the optimal values of auxiliary constants (from Table 4) into the Eq. (3.20), we obtain the AHPM solutions for different values of α at k=0.001.

    Table 4.  The auxiliary control constants for the problem 3.2.
    Aux.Const. α=1 α=0.75 α=0.67
    c1 0.5877657093 0.6018150968 0.2216842316
    c2 0.1517357373 0.1655454563 1.594288158
    c3 0.3855312838 0.3415880346 5.172517408
    c4 0.1250311669 0.10894720410 4.35654487
    c1+c2+c3+c4 1.0000 1.0000 1.0000

     | Show Table
    DownLoad: CSV

    If α=1, then we have

    ˜u(x,y,t)=1.5e3sinh(0.17z)1.3e21t3cosh(0.5z)6.2e16t2sinh(0.5z)6.4e28t4sinh(0.5z)3.4e21t3cosh(1.2z)1.4e21t3cosh(1.2z)2.1e26t4sinh(1.2z)+1.8e24t4cosh(1.3z)+1.1e25t4cosh(0.33z)2.2e23t3cosh(0.17z)4.1e27t4cosh(0.67z)8.9e24t3ccosh(0.17z)+1.4e20t3sinh(0.33z)+2.3e17t2sinh(0.17z)+7.7e20t3sinh(0.67z)+4.6e17t2sinh(0.17z)4.3e29t4sinh(0.17z)+1.4e26t4sinh(1.5z)+5.3e21t3cosh(0.83z)+5.1e16t2sinh(0.83z)+8.4e27t4sinh(0.83z)+2.5e16t2sinh(0.83z)1.2e24t4cosh(z)1.9e19t3sinh(z)3.8e10tcosh(0.17z)(9.0cosh2(0.17z)8)+3.1e17t2sinh(0.17z)(800.0sinh2(0.17z)+766sinh4(0.17z)+133).

    If α=0.75, then we have

    ˜u(x,y,t)=1.5e3sinh(0.17z)3.3e21t2.25cosh(0.5z)3.3e27t3sinh(0.5z)9.1e16t1.5sinh(0.5z)8.3e21t2.25cosh(1.2z)3.6e21t2.25cosh(1.2z)8e26t3sinh(1.2z)+5.6e24t3cosh(1.3z)+3.3e25t3cosh(0.33z)8.2e24t2.25cosh(0.17z)1.3e26t3cosh(0.67z)3.5e24t2.25cosh(0.17z)+3.1e20t(2.25sinh(0.33z)+3.9e17t1.5sinh(0.17z)+1.6e19t2.25sinh(0.67z)7.8e29t3sinh(0.17z)+6.3e17t1.5sinh(0.17z)+5.5e26t3sinh(1.5z)+13e21t2.25cosh(0.83z)+3.4e26t3sinh(0.83z)+11.2e16t1.5sinh(0.83z)3.5e24t3cosh(z)4e19t2.25sinh(z)4.1e10t0.75cosh(0.17z)(9cosh2(0.17z)8)+4.7e17t1.5sinh(0.17z)(800sinh2(0.17z)+766sinh4(0.17z)+133).

    If α=0.67, then we have

    ˜u(x,y,t)=1.5e3sinh(0.17z)2.6e21t2.01cosh(0.5z)9.3e29t2.68sinh(0.5z)5.3e15t1.34sinh(0.5z)3.3e21t2.01cosh(1.2z)5.8e21t2.01cosh(1.2z)2.1e27t2.68sinh(1.2z)+1.4e25t2.68cosh(1.3z)+7.9e27t2.68cosh(0.33z)+1.6e24t2.01cosh(0.17z)3.0e28t2.68cosh(0.67z)+2.7e24t2.01cosh(0.17z)+4.7e20t2.01sinh(0.33z)+9.9e16t1.34sinh(0.17z)+2.5e19t2.01sinh(0.67z)1.4e30t2.68sinh(0.17z)3.9e16t1.34sinh(0.17z)+1.4e27t2.68sinh(1.5z)+10e21t2.01cosh(0.83z)+9.0e28t2.68sinh(0.83z)4.3e15t1.34sinh(0.83z)+1.1e14t1.34sinh(0.83z)8.6e26t2.68cosh(z)6.1e19t2.01sinh(z)4.2e10t0.67cosh(0.17z)(9cosh2(0.17z)8)5.8e17t1.34sinh(0.17z)(800sinh2(0.17z)+766sinh4(0.17z)+133).

    Tables 57 show the AHPM solution, VIM solution, exact solution and absolute error of AHPM solution. The AHPM solution, exact solution and absolute error of AHPM solution are plotted for different values of α, x, y and t in Figures 3 and 4. It is obvious from the Tables 57, Figures 3 and 4, that the AHPM solution of the problem 3.2 is in very good agreement with exact solution.

    Table 5.  Solution of the problem 3.2 for varios values of α, x, y and t at k=0.001.
    x y t VIM [1] (α=0.67) AHPM (α=0.67) VIM [1] (α=0.75) AHPM (α=0.75)
    0.1 0.1 0.2 5.000911707e5 0.000050009117063 5.000913646e5 0.000050009136457
    0.3 5.000907252e5 0.000050009072517 5.000909264e5 0.000050009092629
    0.4 5.000903274e5 0.000050009032711 5.000905240e5 0.00005000905238
    0.6 0.6 0.2 3.020038072e4 0.00030200380721 3.020038341e4 0.00030200383392
    0.3 3.020037458e4 0.00030200374584 3.020037735e4 0.00030200377354
    0.4 3.020036910e4 0.000302003691 3.020037181e4 0.00030200371809
    0.9 0.9 0.2 4.567801693e4 0.00045678016935 4.567802061e4 0.00045678020615
    0.3 4.567800847e4 0.00045678008481 4.567801231e4 0.00045678012298
    0.4 4.567800092e4 0.00045678000927 4.567800467e4 0.0004567800466

     | Show Table
    DownLoad: CSV
    Table 6.  Solution and absolute error of the problem 3.2 for various values of x, y and t at k=0.001 and α=1.
    x y t VIM [1] AHPM Exact
    0.1 0.1 0.2 5.000918398e5 0.000050009183981 4.995923204e5
    0.3 5.000914609e5 0.000050009146085 4.993421817e5
    0.4 5.000910820e5 0.000050009108189 4.990920434e5
    0.6 0.6 0.2 3.020038992e4 0.0003020038994 3.019530008e4
    0.3 3.020038472e4 0.00030200384719 3.019274992e4
    0.4 3.020037950e4 0.00030200379498 3.019019978e4
    0.9 0.9 0.2 4.567802964e4 0.00045678029634 4.567281735e4
    0.3 4.567802242e4 0.00045678022442 4.567020404e4
    0.4 4.567801525e4 0.00045678015251 4.566759074e4

     | Show Table
    DownLoad: CSV
    Table 7.  AHPM solution and exact solution and absolute error of AHPM solution of the problem 3.2 for various values of α, x, y and t at k=0.001.
    x y t AHPM (α=0.67) AHPM (α=0.75) AHPM (α=1) Exact (α=1) Error
    1 1 0.2 0.00050931053201 0.0005093105733 0.0005093106745 0.00050925803257 5.26e8
    0.4 0.00050931035239 0.00050931039427 0.00050931051311 0.00050920522983 1.05e7
    0.6 0.00050931020147 0.00050931023732 0.00050931035172 0.00050915242765 1.58e7
    0.8 0.00050931006661 0.00050931009319 0.00050931019033 0.00050909962604 2.11e7
    1 0.00050930994256 0.00050930995788 0.00050931002895 0.00050904682499 2.63e7
    5 5 0.2 0.003829187741 0.0038291908792 0.00382919857 0.0038290737537 1.25e7
    0.4 0.0038291740912 0.0038291772737 0.0038291863046 0.003828936676 2.5e7
    0.6 0.0038291626226 0.0038291653465 0.0038291740396 0.0038287996024 3.74e7
    0.8 0.0038291523746 0.0038291543934 0.003829161775 0.0038286625332 4.99e7
    1 0.0038291429482 0.003829144112 0.0038291495107 0.0038285254682 6.24e7
    10 10 0.2 0.020993470055 0.020993944094 0.02099510678 0.020996261505 1.15e6
    0.4 0.020991408888 0.020991888681 0.02099325193 0.020995559858 2.31e6
    0.6 0.020989679031 0.020990088717 0.020991398624 0.020994858233 3.46e6
    0.8 0.020988134787 0.020988437313 0.020989546856 0.020994156633 4.61e6
    1 0.020986715582 0.02098688854 0.020987696623 0.020993455055 5.76e6

     | Show Table
    DownLoad: CSV
    Figure 3.  AHPM solution, exact solution and absolute error of AHPM solution of Problem 3.2 at α = 1 and t = 0.2 when k = 0.001.
    Figure 4.  AHPM solution, exact solution and absolute error of AHPM solution of Problem 3.2 at α = 1 and k = 0.0001.

    In this article, asymptotic homotopy perturbation method (AHPM) is developed to solve non-linear fractional models. It is a different procedure from the procedures of HAM, HPM and OHAM. The two special cases, ZK(2,2,2) and ZK(3,3,3) of fractional Zakharov-Kuznetsov model are considered to illustrate a very simple procedure of the homotopy methods. The numerical results in simulation section of AHPM solutions are more accurate to the exact solutions as comparing with fractional complex transform (FCT) using variational iteration method (VIM). In the field of fractional calculus, it is necessary to introduce various procedures and schemes to compute the solution of non-linear fractional models. In this concern, we expect that this new proposed procedure is a best effort. The best improvement and the application of this new procedures to the solution of advanced non-linear fractional models with computer software codes will be our further consideration.

    The authors would like to thank anonymous referees for their careful corrections to and valuable comments on the original version of this paper.

    The authors declare no conflict of interest.



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