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Research article

Bald eagle search algorithm for solving a three-dimensional path planning problem


  • Three-dimensional path planning refers to determining an optimal path in a three-dimensional space with obstacles, so that the path is as close to the target location as possible, while meeting some other constraints, including distance, altitude, threat area, flight time, energy consumption, and so on. Although the bald eagle search algorithm has the characteristics of simplicity, few control parameters, and strong global search capabilities, it has not yet been applied to complex three-dimensional path planning problems. In order to broaden the application scenarios and scope of the algorithm and solve the path planning problem in three-dimensional space, we present a study where five three-dimensional geographical environments are simulated to represent real-life unmanned aerial vehicles flying scenarios. These maps effectively test the algorithm's ability to handle various terrains, including extreme environments. The experimental results have verified the excellent performance of the BES algorithm, which can quickly, stably, and effectively solve complex three-dimensional path planning problems, making it highly competitive in this field.

    Citation: Yunhui Zhang, Yongquan Zhou, Shuangxi Chen, Wenhong Xiao, Mingyu Wu. Bald eagle search algorithm for solving a three-dimensional path planning problem[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2856-2878. doi: 10.3934/mbe.2024127

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  • Three-dimensional path planning refers to determining an optimal path in a three-dimensional space with obstacles, so that the path is as close to the target location as possible, while meeting some other constraints, including distance, altitude, threat area, flight time, energy consumption, and so on. Although the bald eagle search algorithm has the characteristics of simplicity, few control parameters, and strong global search capabilities, it has not yet been applied to complex three-dimensional path planning problems. In order to broaden the application scenarios and scope of the algorithm and solve the path planning problem in three-dimensional space, we present a study where five three-dimensional geographical environments are simulated to represent real-life unmanned aerial vehicles flying scenarios. These maps effectively test the algorithm's ability to handle various terrains, including extreme environments. The experimental results have verified the excellent performance of the BES algorithm, which can quickly, stably, and effectively solve complex three-dimensional path planning problems, making it highly competitive in this field.



    In this paper, we are concerned with the sharp decay rates of solutions to the Cauchy problem for the isentropic Navier-Stokes equations:

    {tρ+div(ρu)=0,(t,x)R+×R3,t(ρu)+div(ρuu)+p(ρ)=divT,(t,x)R+×R3,lim|x|ρ=ˉρ,lim|x|u=0,tR+,(ρ,u)|t=0=(ρ0,u0),xR3, (1.1)

    which governs the motion of a isentropic compressible viscous fluid. The unknown functions ρ and u represent the density and velocity of the fluid respectively. The pressure p=p(ρ) is a smooth function in a neighborhood of a positive constant ˉρ s.t. p(ˉρ)>0. T is the viscosity stress tensor given by T=μ(u+(u)t)+ν(divu)I with I the identity matrix. We assume that the constant viscosity coefficients μ>0 and ν satisfy ν+23μ>0. Throughout this article, by optimal time decay rate, we refer to the best possible decay rate in upper bound as many literatures, and the sharp time decay rate includes the best possible upper and lower bounds.

    Using the classical spectral method, the optimal time decay rate (upper bound) of the linearized equations of the isentropic Navier-Stokes equations are well known. One may then expect that the small solution of the nonlinear equations (1.1) have the same decay rate as the linear one. Our work is devoted to proving the sharp time decay rate (for both upper and lower bound) for the nonlinear system.

    In the case of one space dimension, Zeng [24] and Liu-Zeng [15] offered a detailed analysis of the solution to a class of hyperbolic-parabolic system through point-wise estimate, including the isentropic Navier-Stokes system. For multi-dimensional Navier-Stokes equations (and/or Navier-Stokes-Fourier system), the Hs global existence and time-decay rate of strong solutions with the initial perturbation small in HsL1 are obtained in whole space first by A. Matsumura and T. Nishida [17], [18]. When the small initial perturbation belongs to H3 only, using a weighted energy method, A. Matsumura [16] showed the time-decay rate (1+t)34 of upper bound in L-norm. Since then, there are concrete development on the upper bound time-decay estimates: the optimal Lp (with 2p) upper bound decay rate was proved by G. Ponce [19], combining the spectral analysis on linearized system and the energy method for small initial perturbation in L1. For the isentropic Navier-Stokes equations with artificial viscosity, D. Hoff and K. Zumbrun [6], [7] studied the Green's function and derived the Lp (1p) upper bound time decay rate of diffusive waves for the small initial perturbation belongs to HmL1 with m4. Liu and Wang [14] studied the point-wise estimates of the Green function of the linearized isentropic Navier-Stokes system in 3D and then analyzed the coupling of nonlinear diffusion waves, obtained the optimal (upper bound) decay rate. These results were further extended to the exterior problem [12], [11], or the half space problem [9], [10], [8]. Recently, Guo and Wang in [5] developed a new general energy method for proving the optimal (upper bound) time decay rates of the solutions to the dissipative equations in the whole space, using a family of scaled energy estimates with minimum derivative counts and interpolations among them without linear decay analysis.

    When additional external force is taken into account, the external force does affect the long time behavior of solutions. The upper bound of time decay rates were studied intensively, see for instance [1] and [2] on unbounded domain, [22], [23] on the convergence of the non-stationary flow to the corresponding steady flow when the initial date are small in H3L65, and [4], [3], on the optimal LpLq upper bound decay rates for potential forces.

    The main goal of current paper is to establish the sharp decay rate, on both upper and lower bounds, to the solutions of (1.1) using relatively simple energy method. We remark that similar results had been pursued by M. Schonbek [20], [21] for incompressible Navier-Stokes equations, and by Li, Matsumura-Zhang [13] for isentropic Navier-Stokes-Poisson system. Although they share the same spirit in obtaining the lower bound decay rates, the feature of the spectrum near zero exhibits quite different behaviors, leading to different analysis. For instance, we explored the elegant structure of the higher order nonlinear terms of Navier-Stokes, when choosing conservative variables: density and momentum. The conservative form of the sharp equations provided a natural derivative structure in these terms, leading to the possibility of a faster decay rate estimate. We will make a more detailed comparison later in this paper.

    Define n=ρˉρ, and let m=ρu=(n+ˉρ)u be the momentum. We rewrite (1.1) as

    {tn+divm=0,(t,x)R+×R3,tm+c2nˉμm(ˉμ+ˉν)divm=F,(t,x)R+×R3,lim|x|n=0,lim|x|m=0,tR+,(n,m)|t=0=(ρ0ˉρ,ρ0u0),xR3, (1.2)

    where ˉμ=μˉρ, ˉν=νˉρ, c=p(ˉρ)>0 is the sound speed, and

    F=div{mmn+ˉρ+ˉμ(nmn+ˉρ)}{(ˉμ+ˉν)div(nmn+ˉρ)+(p(n+ˉρ)p(ˉρ)c2n)}.

    It is this structure of F that plays an important role in our analysis.

    Our aim is to obtain a clear picture of the large time behavior of U=(n,m) in L2(R3) when U0=(ρ0ˉρ,ρ0u0) is sufficiently smooth and small. We introduce the following initial value problem of the linearized Navier-Stokes system corresponding to (1.2):

    {t˜n+div˜m=0,(t,x)R+×R3,t˜m+c2˜nˉμ˜m(ˉμ+ˉν)div˜m=0,(t,x)R+×R3,lim|x|˜n=0,lim|x|˜m=0,tR+,(˜n,˜m)|t=0=(ρ0ˉρ,ρ0u0),xR3, (1.3)

    where ˉμ=μˉρ, ˉν=νˉρ, c=p(ˉρ). It is known that the L2-norm of ˜U=(˜n,˜m) decays at the optimal upper bound rate (1+t)34 for generic small initial data, see for instance [18]. A detailed proof on the optimal lower and upper bound rate will be given in the section 3 of this paper. In section 4, we prove that (U˜U)(,t)L2 decays at a faster rate than ˜U(,t)L2, under some reasonable conditions on the initial data. Therefore, U(,t)L2 shares the sharp decay rate of (1+t)34.

    Notation. For ab, we mean that there is a uniform constant C, which may be different on different lines, such that aCb. And ab stands for ab and ba.

    We now state our main result.

    Theorem 1.1. Assume that (n0,m0)L1(R3)H3(R3), δ0=:(n0,m0)L1(R3)H3(R3) is sufficiently small, and

    R3(n0,m0)dx0, (1.4)

    then there is a unique global classical solution ˜U=(˜n,˜m)C([0,);H3(R3)) of the linearized system (1.3) satisfying for some positive constant C

    C1(1+t)34k2k˜n(t)L2(R3)C(1+t)34k2,k=0,1,2,3,C1(1+t)34k2k˜m(t)L2(R3)C(1+t)34k2,k=0,1,2,3,

    and the initial value problem (1.2) has a unique solution U=(n,m)C([0,);H3(R3)). Moreover, let nh=n˜n and mh=m˜m, then it holds that

    k(nh,mh)(t)L2(R3)δ20(1+t)54k2,k=0,1,2,3mh(t)L2(R3)δ20(1+t)114,3nh(t)L2(R3)δ0(1+t)74.

    As a consequence, there exists a positive constant C1 such that

    C11(1+t)34k2kn(t)L2(R3)C1(1+t)34k2,k=0,1,2,C11(1+t)34k2km(t)L2(R3)C1(1+t)34k2,k=0,1,2,3.

    Remark 1.1. We remark that this theorem is valid under the condition (1.4) which is important in the lower bound estimate to the linearized problem. When (1.4) fails, the decay rate of the linearized system (1.3) depends on the order of the degeneracy of moments. Assume (n0,m0)L1H3 and belong to certain appropriate weighted Lp spaces, similar situation happened also in the incompressible Navier-Stokes equations, c.f. [20], [21]. We also note that our condition (1.4) is weaker than those in most of previous results where the differentiability of Fourier transform of initial disturbance is required in general.

    Remark 1.2. In [13], Li, Matsumura-Zhang proved the lower bound decay rate of the linearized isentropic Navier-Stokes-Poisson system, they only require |ˆn0(ξ)|>c0>0 for |ξ|1 with c0 a constant due to the special structure of the spectrum from the help of the Poisson term. This condition is proposed in Fourier space, similar to (1.4) in some sense. In our case, the spectrum is different and the different structure leads to different sharp decay rates.

    In what follows, we will set n=ρˉρ, u=u0. We rewrite (1.1) in the perturbation form as

    {tn+ˉρdivu=ndivuun,tu+γˉρnˉμu(ˉμ+ˉν)divu=uuˉμf(n)u(ˉμ+ˉν)f(n)divug(n)n,lim|x|n=0,lim|x|u=0,(n,u)|t=0=(ρ0ˉρ,u0), (2.1)

    where ˉμ=μˉρ, ˉν=νˉρ, γ=p(ˉρ)ˉρ2, and the nonlinear functions f and g are defined by

    f(n):=nn+ˉρ,g(n):=p(n+ˉρ)n+ˉρp(ˉρ)ˉρ. (2.2)

    We assume that there exist a time of existence T>0 and sufficiently small δ>0, such that a priori estimate

    n(t)H3+u(t)H3δ, (2.3)

    holds for any t[0,T]. First of all, by (2.3) and Sobolev's inequality, we obtain that

    ˉρ2n+ˉρ2ˉρ.

    Hence, we immediately have

    |f(n)|,|g(n)|C|n|,|kf(n)|,|kg(n)|CkN+, (2.4)

    where f(n) and g(n) are nonlinear functions of n defined by (2.2).

    Next, we begin with the energy estimates including n and u themselves. The following results is essentially due to A. Matsumura and T. Nishida [17], [18].

    Theorem 2.1. Assume that (n0,u0)H3(R3), then there exists a constant δ0>0 such that if

    n0H3+u0H3δ0,

    then the problem (2.1) admits a unique global solution (n(t),u(t)) satisfying that for all t0,

    n(t)2H3+u(t)2H3+t0(n(τ)2H2+u(τ)2H3)dτC(n02H3+u02H3),

    where C is a positive constant independent of time.

    The proof of this theorem is divided into several subsections.

    For k=0, multiplying the first equation in (2.1) by γn and the second equation in (2.1) by u, summing up and then integrating the result over R3 by parts. By virtue of Hölder's inequality, Sobolev's inequality and the fact (2.4), we obtain that

    12ddtR3(γ|n|2+|u|2)dx+R3(ˉμ|u|2+(ˉμ+ˉν)|divu|2)dx=R3γ(ndivuun)n(uu+ˉμf(n)u+(ˉμ+ˉν)f(n)divu+g(n)n)udxnL3uL2nL6+(uL3uL2+nL3nL2)uL6+(uLnL2+nLuL2)uL2(nL3+uL3+nL+uL)(n2L2+u2L2). (2.5)

    Now for 1k3, applying k to (2.1) and then multiplying the first equation by γkn and the second equation by ku, summing up and integrating over R3. For k=1 we have

    12ddtR3(γ|n|2+|u|2)dx+R3(ˉμ|2u|2+(ˉμ+ˉν)|divu|2)dx(nL+uL+nL+uL)(n2L2+u2L2+2u2L2). (2.6)

    For k=2 we have

    12ddtR3(γ|2n|2+|2u|2)dx+R3(ˉμ|3u|2+(ˉμ+ˉν)|2divu|2)dx(nL+uL+nL+uL)(2n2L2+2u2L2+3u2L2). (2.7)

    For k=3 we have

    12ddtR3(γ|3n|2+|3u|2)dx+R3(ˉμ|4u|2+(ˉμ+ˉν)|3divu|2)dx(nL+uL+nL+uL)(3n2L2+3u2L2+4u2L2)+nL34uL22nL6+uL34uL22uL6+2nL3(3nL2+4uL2)2uL6. (2.8)

    Summing up the above estimates, noting that δ>0 is small, we obtain that

    ddt0k3(γkn2L2+ku2L2)+C11k4ku2L2C2δ1k3kn2L2. (2.9)

    For 0k2, applying k to the second equation in (2.1) and then multiplying by k+1n. The key idea is to integrate by parts in the t-variable and to use the continuity equation. Thus integrating the results by parts for both the t- and x-variables, we obtain for k=0 that

    ddtR3undx+γˉρR3|n|2dxu2L2+nL22uL2+(nL+uL)(n2L2+u2L2), (2.10)

    for k=1, we get

    ddtR3u2ndx+γˉρR3|2n|2dx2u2L2+2nL23uL2+((n,u)L+(n,u)L)×(n2L2+2n2L2+2u2L2), (2.11)

    and for k=2 we have

    ddtR32u3ndx+γˉρR3|3n|2dx3u2L2+3nL24uL2+((n,u)L+(n,u)L)×(2n2L2+2u2L2+3n2L2+3u2L2). (2.12)

    Plugging the above estimates, using the smallness of δ>0, we obtain that

    ddt0k2R3kuk+1ndx+C31k3kn2L2C41k4ku2L2. (2.13)

    Proof of Theorem 2.1. Multiplying (2.13) by 2C2δC3, adding it with (2.9), with the help of smallness of δ>0, we deduce that there exists a constant C5>0 such that

    ddt{0k3(γkn2L2+ku2L2)+2C2δC30k2R3kuk+1ndx}+C5{1k3kn2L2+1k4ku2L2}0. (2.14)

    Next, we define E(t) to be C15 times the expression under the time derivative in (2.14). Then we may write (2.14) as

    ddtE(t)+n(t)2H2+u(t)2H30. (2.15)

    Observe that since δ is small, then there exists a constant C6>0 such that

    C16(n(t)2H3+u(t)2H3)E(t)C6(n(t)2H3+u(t)2H3).

    Then integrating (2.15) directly in time, we get

    sup0tT(n(t)2H3+u(t)2H3)+C6T0(n(τ)2H2+u(τ)2H3)dτC26(n02H3+u02H3).

    Using a standard continuity argument along with classical local wellposedness theory, this closes the a priori assumption (2.3) if we assume n0H3+u0H3δ0 is sufficiently small. We can then extend the solution globally in time and complete the proof of Theorem 2.1.

    In this section, we consider the initial value problem for the linearized Navier-Stokes system

    {t˜n+div˜m=0,(t,x)R+×R3,t˜m+c2˜nˉμ˜m(ˉμ+ˉν)div˜m=0,(t,x)R+×R3,lim|x|˜n=0,lim|x|˜m=0,tR+,(˜n,˜m)|t=0=(ρ0ˉρ,ρ0u0),xR3, (3.1)

    where ˉμ=μˉρ, ˉν=νˉρ, c=p(ˉρ).

    In terms of the semigroup theory for evolutionary equations, the solution (˜n,˜m) of the linearized Navier-Stokes problem (3.1) can be expressed for ˜U=(˜n,˜m)t as

    ˜Ut=B˜U,t0,˜U(0)=˜U0,

    which gives rise to

    ˜U(t)=S(t)˜U0=etB˜U0,t0,

    where B is defined as

    B=(0divc2ˉμangle+(ˉμ+ˉν)div).

    What left is to analyze the differential operator B in terms of its Fourier expression A(ξ) and show the long time properties of the semigroup S(t). Applying the Fourier transform to system (3.1), we have

    tˆ˜U(t,ξ)=A(ξ)ˆ˜U(t,ξ),t0,ˆ˜U(0,ξ)=ˆ˜U0(ξ),

    where ξ=(ξ1,ξ2,ξ3)t, and A(ξ) is defined as

    A(ξ)=(0iξtc2iξˉμ|ξ|2I3×3(ˉμ+ˉν)ξξ).

    The eigenvalues of the matrix A can be computed by

    det(A(ξ)λI)=(λ+ˉμ|ξ|2)2(λ2+(2ˉμ+ˉν)|ξ|2λ+c2|ξ|2)=0,

    which implies

    λ0=ˉμ|ξ|2(double),λ1=λ1(|ξ|),λ2=λ2(|ξ|).

    The semigroup etA is expressed as

    etA=eλ0tP0+eλ1tP1+eλ2tP2,

    where the project operators Pi can be computed as

    Pi=ijA(ξ)λjIλiλj.

    By a direct computation, we can verify the exact expression for the Fourier transform ˆG(t,ξ) of Green's function G(t,x)=etB as

    ˆG(t,ξ)=etA=(λ1eλ2tλ2eλ1tλ1λ2iξt(eλ1teλ2t)λ1λ2c2iξ(eλ1teλ2t)λ1λ2eλ0t(Iξξ|ξ|2)+ξξ|ξ|2λ1eλ1tλ2eλ2tλ1λ2)=(ˆNˆM).

    Indeed, we can make the following decomposition for (˜n,˜m)=G˜U0 as

    ˆ˜n=ˆNˆ˜U0=(ˆN+ˆN)ˆ˜U0,ˆ˜m=ˆMˆ˜U0=(ˆM+ˆM)ˆ˜U0,

    where

    ˆN=(λ1eλ2tλ2eλ1tλ1λ20),ˆN=(0iξt(eλ1teλ2t)λ1λ2),ˆM=(c2iξ(eλ1teλ2t)λ1λ20),ˆM=(0eλ0t(Iξξ|ξ|2)+ξξ|ξ|2λ1eλ1tλ2eλ2tλ1λ2).

    We further decompose the Fourier transform ˆN, ˆM into low frequency term and high frequency term below.

    Define

    ˆN=ˆN1+ˆN2,ˆN=ˆN1+ˆN2,ˆM=ˆM1+ˆM2,ˆM=ˆM1+ˆM2,

    where ()1=χ(ξ)(), ()2=(1χ(ξ))(), and χ(ξ) is a smooth cut off function such that

    χ(ξ)={1,|ξ|R,0,|ξ|R+1.

    Then we have the following decomposition for (˜n,˜m)=G˜U0 as

    ˆ˜n=ˆNˆ˜U0=ˆN1ˆ˜U0+ˆN2ˆ˜U0=(ˆN1+ˆN1)ˆ˜U0+(ˆN2+ˆN2)ˆ˜U0,ˆ˜m=ˆMˆ˜U0=ˆM1ˆU0+ˆM2ˆ˜U0=(ˆM1+ˆM1)ˆ˜U0+(ˆM2+ˆM2)ˆ˜U0. (3.2)

    To derive the long time decay rate of solution, we need to use accurate approximation to the Fourier transform ˆG(t,x) of Green's function for both lower frequency and high frequency. In terms of the definition of the eigenvalues, we are able to obtain that it holds for |ξ|η for some small positive constant η that

    λ1=2ˉμ+ˉν2|ξ|2+i24c2|ξ|2(2ˉμ+ˉν)2|ξ|4=a+bi,λ2=2ˉμ+ˉν2|ξ|2i24c2|ξ|2(2ˉμ+ˉν)2|ξ|4=abi, (3.3)

    and we have

    λ1eλ2tλ2eλ1tλ1λ2=e12(2ˉμ+ˉν)|ξ|2t[cos(bt)+12(2ˉμ+ˉν)|ξ|2sin(bt)b]O(1)e12(2ˉμ+ˉν)|ξ|2t,|ξ|η,
    λ1eλ1tλ2eλ2tλ1λ2=e12(2ˉμ+ˉν)|ξ|2t[cos(bt)12(2ˉμ+ˉν)|ξ|2sin(bt)b]O(1)e12(2ˉμ+ˉν)|ξ|2t,|ξ|η,
    eλ1teλ2tλ1λ2=e12(2ˉμ+ˉν)|ξ|2tsin(bt)bO(1)1|ξ|e12(2ˉμ+ˉν)|ξ|2t,|ξ|η,

    where

    b=124c2|ξ|2(2ˉμ+ˉν)2|ξ|4c|ξ|+O(|ξ|3),|ξ|η.

    For the high frequency |ξ|η, we are also able to obtain that it holds for |ξ|η that

    λ1=2ˉμ+ˉν2|ξ|212(2ˉμ+ˉν)2|ξ|44c2|ξ|2=ab,λ2=2ˉμ+ˉν2|ξ|2+12(2ˉμ+ˉν)2|ξ|44c2|ξ|2=a+b, (3.4)

    and we have

    λ1eλ2tλ2eλ1tλ1λ2=12e(a+b)t[1+e2bt]a2be(a+b)t[1e2bt]O(1)eR0t,|ξ|η,
    λ1eλ1tλ2eλ2tλ1λ2=a+b2be(a+b)t[1e2bt]+e(ab)tO(1)eR0t,|ξ|η,
    eλ1teλ2tλ1λ2=12be(a+b)t[1e2bt]O(1)1|ξ|2eR0t,|ξ|η,

    where

    b=12(2ˉμ+ˉν)2|ξ|44c2|ξ|212(2ˉμ+ˉν)|ξ|22c22ˉμ+ˉν+O(|ξ|2),|ξ|η.

    Here R0, η are some fixed positive constants.

    In this section, we apply the spectral analysis to the semigroup for the linearized Navier-Stokes system. We will establish the L2 and Lp (2p) time decay rate of the global solutions for the linearized Navier-Stokes system.

    With the help of the formula for Green's function in Fourier space and the asymptotic analysis on its elements, we are able to establish the L2 time decay rate. Indeed, we have the L2-time decay rate of the global strong solution to the problem for the linearized Navier-Stokes system as follows.

    Proposition 4.1. Let U0=(n0,m0)L1(R3)Hl(R3) with l3, then (˜n,˜m) solves the linearized Navier-Stokes system (3.1) and satisfies for 0kl that

    k(˜n,˜m)(t)L2(R3)C(1+t)34k2(U0L1(R3)+kU0L2(R3)),

    where C is a positive constant independent of time.

    Proof. A straightforward computation together with the formula of the Green's function ˆG(t,ξ) gives

    ˆ˜n(t,ξ)=λ1eλ2tλ2eλ1tλ1λ2ˆn0iξˆm0(eλ1teλ2t)λ1λ2{O(1)e12(2ˉμ+ˉν)|ξ|2t(|ˆn0|+|ˆm0|),|ξ|η,O(1)eR0t(|ˆn0|+|ˆm0|),|ξ|η,ˆ˜m(t,ξ)=c2iξ(eλ1teλ2t)λ1λ2ˆn0+eλ0tˆm0+(λ1eλ1tλ2eλ2tλ1λ2eλ0t)ξ(ξˆm0)|ξ|2{O(1)eˉμ|ξ|2t(|ˆn0|+|ˆm0|),|ξ|η,O(1)eR0t(|ˆn0|+|ˆm0|),|ξ|η,

    here and below, R0, η are some fixed positive constants. Therefore, we have the L2-decay rate for (˜n,˜m) as

    (ˆ˜n,ˆ˜m)(t)2L2(R3)=|ξ|η|(ˆ˜n,ˆ˜m)(t,ξ)|2dξ+|ξ|η|(ˆ˜n,ˆ˜m)(t,ξ)|2dξ|ξ|ηe2ˉμ|ξ|2t(|ˆn0|2+|ˆm0|2)dξ+|ξ|ηe2R0t(|ˆn0|2+|ˆm0|2)dξ(1+t)32(n0,m0)2L1(R3)L2(R3).

    And the L2-decay rate on the derivatives of (˜n,˜m) as

    (^k˜n,^k˜m)(t)2L2(R3)=|ξ|η|ξ|2k|(ˆ˜n,ˆ˜m)(t,ξ)|2dξ+|ξ|η|ξ|2k|(ˆ˜n,ˆ˜m)(t,ξ)|2dξ|ξ|ηe2ˉμ|ξ|2t|ξ|2k(|ˆn0|2+|ˆm0|2)dξ+|ξ|ηe2R0t|ξ|2k(|ˆn0|2+|ˆm0|2)dξ(1+t)32k((n0,m0)2L1(R3)+(kn0,km0)2L2(R3)).

    The proof of the Proposition 4.1 is completed.

    It should be noted that the L2-time decay rates derived above are optimal.

    Proposition 4.2. Let U0=(n0,m0)L1(R3)Hl(R3) with l3, assume that Mn=R3n0(x)dx and Mm=R3m0(x)dx satisfies that Mn, Mm are at least not all zeros, then the solution (˜n,˜m) of the linearized Navier-Stokes system (3.1) given by Proposition 4.1 satisfies for 0kl

    C1(1+t)34k2k˜n(t)L2(R3)C(1+t)34k2,C1(1+t)34k2k˜m(t)L2(R3)C(1+t)34k2,

    where C is a positive constant independent of time.

    Proof. We only show the case of k=0 for simplicity, the argument applies to the other orders of derivatives. From the formula of the Green's function ˆG(t,ξ), we deduce that

    ˆ˜n(t,ξ)=λ1eλ2tλ2eλ1tλ1λ2ˆn0iξˆm0(eλ1teλ2t)λ1λ2=e12(2ˉμ+ˉν)|ξ|2t[cos(bt)ˆn0iξˆm0sin(bt)b]+e12(2ˉμ+ˉν)|ξ|2t[12(2ˉμ+ˉν)|ξ|2sin(bt)bˆn0]=T1+T2,for|ξ|η,
    ˆ˜m(t,ξ)=c2iξ(eλ1teλ2t)λ1λ2ˆn0+eλ0tˆm0+(λ1eλ1tλ2eλ2tλ1λ2eλ0t)ξ(ξˆm0)|ξ|2=[e12(2ˉμ+ˉν)|ξ|2t[cos(bt)ξ(ξˆm0)|ξ|2c2iξsin(bt)bˆn0]+eˉμ|ξ|2t[ˆm0ξ(ξˆm0)|ξ|2]]e12(2ˉμ+ˉν)|ξ|2t[12(2ˉμ+ˉν)|ξ|2sin(bt)bξ(ξˆm0)|ξ|2]=S1+S2,for|ξ|η,

    here and below, η is a sufficiently small but fixed constant.

    It is easy to check that

    ˆ˜n(t,ξ)2L2=|ξ|η|ˆ˜n(t,ξ)|2dξ+|ξ|η|ˆ˜n(t,ξ)|2dξ|ξ|η|T1+T2|2dξ|ξ|η12|T1|2|T2|2dξ. (4.1)

    We then calculate that

    |ξ|η|T2|2dξˆn02L|ξ|ηe(2ˉμ+ˉν)|ξ|2t|ξ|4(sin(bt)b)2dξˆn02L|ξ|ηe(2ˉμ+ˉν)|ξ|2t|ξ|2dξ(1+t)52n02L1. (4.2)

    Since n0(x)L1 implies ˆn0(ξ)C(R3). If ˆn0(0)=R3n0(x)dx0, we deduce that ˆn0(ξ)0 for |ξ|η when η is sufficiently small. One finds that, when Mn0,

    |ˆn0(ξ)|21C|R3n0(x)dx|2M2nC,for|ξ|η.

    For ˆm0, a similar argument yields that, when Mm0, we have

    |ξˆm0(ξ)|2|ξ|2|ξMm|2C|ξ|2,for|ξ|η.

    When Mn0, Mm0, with the help of the above analysis, using bc|ξ|+O(|ξ|3) for |ξ|η, we obtain that

    |ξ|η|T1|2dξM2nC|ξ|ηe(2ˉμ+ˉν)|ξ|2tcos2(bt)dξ+1C|ξ|η|ξMm|2b2e(2ˉμ+ˉν)|ξ|2tsin2(bt)dξ
    min{M2n,M2m3c2}C|ξ|ηe(2ˉμ+ˉν)|ξ|2t(cos2(bt)+sin2(bt))dξC1|ξ|ηe(2ˉμ+ˉν)|ξ|2tdξC1(1+t)32. (4.3)

    If Mn0, Mm=0, and by the conituinity of ˆm0 near ξ=0, there exists a small enough constant ϵ such that ϵ0 as ξ0, and

    |ˆm0(ξ)|2<ϵ,for|ξ|η.

    We thus use the help of spherical coordinates and the change of variables r=|ξ|t to obtain that

    |ξ|η|T1|2dξM2nC|ξ|ηe(2ˉμ+ˉν)|ξ|2tcos2(bt)dξϵCc2|ξ|ηe(2ˉμ+ˉν)|ξ|2tsin2(bt)dξM2nCt32ηt0e(2ˉμ+ˉν)r2cos2(crt)r2drϵCc2t32ηt0e(2ˉμ+ˉν)r2sin2(crt)r2drM2nCt32[cηtπ]1k=0kπ+π4ctkπcte(2ˉμ+ˉν)r2cos2(crt)r2drϵCc2(1+t)32M2n2Ct32[cηtπ]1k=0kπ+π4ctkπcte(2ˉμ+ˉν)r2r2drϵCc2(1+t)32C11(1+t)32C12ϵ(1+t)32.C1(1+t)32 (4.4)

    In the case of Mn=0, Mm0, we can use a similar argument to obtain that

    |ξ|η|T1|2dξϵC|ξ|ηe(2ˉμ+ˉν)|ξ|2tcos2(bt)dξ+M2m3Cc2|ξ|ηe(2ˉμ+ˉν)|ξ|2tsin2(bt)dξC1(1+t)32. (4.5)

    Combining the above estimates (4.1), (4.2), (4.3), (4.4) and (4.5), we obtain the lower bound of the time decay rate for ˜n(t,x) as

    ˜n(t,x)2L2=ˆ˜n(t,ξ)2L2C1(1+t)32.

    The lower bound of the time decay rate for ˜m(t,x) can be shown in a similar fashion. It is not difficult to derive that

    ˆ˜m(t,ξ)2L2|ξ|η12|S1|2|S2|2dξ, (4.6)

    then we find that

    |ξ|η|S2|2dξ(1+t)52m02L1. (4.7)

    We then calculate that

    |ξ|η|S1|2dξ{c4M2nC|ξ|η|ξ|2b2e(2ˉμ+ˉν)|ξ|2tsin2(bt)dξ+1C|ξ|η|ξMm|2|ξ|2e(2ˉμ+ˉν)|ξ|2tcos2(bt)dξ}+{|ξ|ηe12(4ˉμ+ˉν)|ξ|2tcos(bt)ξ(ξˆm0)|ξ|2(ˆm0ξ(ξˆm0)|ξ|2)dξ}=J1+J2.

    A direct computation gives rise to

    J1C1(1+t)32,J2=0. (4.8)

    Combining the above estimates (4.6), (4.7) and (4.8), we obtain the lower bound of the time decay rate for ˜m(t,x) as

    ˜m(t,x)2L2=ˆ˜m(t,ξ)2L2C1(1+t)32.

    Then the proof of Proposition 4.2 is completed.

    In this subsection, we establish the following Lp-time decay rate of the global strong solution to the linearized Navier-Stokes system with p[2,+].

    Proposition 4.3. Let U0=(n0,m0)L1(R3)Wl,p(R3) with l3, then (˜n,˜m) solves the linearized Navier-Stokes system (3.1) and satisfies for 0kl and p[2,+] that

    k(˜n,˜m)(t)Lp(R3)C(1+t)32(11p)k2(U0L1(R3)+kU0Lp(R3)),

    where C is a positive constant independent of time.

    To prove Proposition 4.3, the following two lemmas in [6] are helpful.

    Lemma 4.1. Let n1 and assume that ˆf(ξ)LCn+1(Rn/{0}), with

    |αξˆf(ξ)|C{|ξ||α|+σ1,|ξ|R,|α|=n,|ξ||α|σ2,|ξ|R,|α|=n1,n,n+1,

    where σ1,σ2>0 and n>22σ2. Then ˆf(ξ) is continuous at 0 and , and

    f=m1+m2δ,

    where m1L1(Rn) satisfies m1L1(Rn)C(C), m2 is the constant

    m2=(2π)n2lim|ξ|ˆf(ξ),

    and δ is the Dirac distribution. In particular, ˆf(ξ) is a strong Lp multiplier, 1p, in the sense that, for any gLp,

    fgLpCgLp,1p,

    where C depends only on |m2|ˆfL and the constant C above.

    Lemma 4.2. Let ˆg(t,ξ)=ˆK(t,ξ)ˆf(ξ), where ˆK(t,ξ)=eϑ|ξ|2t, ˆf(ξ)LCn+1(Rn), and

    |βξˆf(ξ)|C|ξ||β|,|β|n+1.

    Then αxg(t,)Lp for t>0, and for all α, 1p, we have

    αxg(t,)LpC(|α|)tn2(11p)|α|2.

    In particular, ^αxg(t,x)=(iξ)αˆg(t,ξ) is a strong Lp multiplier, with norm bounded by C(|α|,ϑ)Ct|α|2, where the constant C(|α|,ϑ) depends only on |α| and ϑ.

    Now let us turn to the proof of Proposition 4.3.

    Proof of Proposition 4.3. We first analyze above higher frequency terms denoted by ^()2. Recall that

    λ1=(2ˉμ+ˉν)|ξ|2+2c22ˉμ+ˉν+O(|ξ|2),λ2=2c22ˉμ+ˉν+O(|ξ|2),|ξ|η.

    We shall prove that the higher frequency terms are Lp Fourier multipliers with an exponential time decay coefficient Cec1t for some constants c1>0. For simplicity, we only show that ˆN2 is an Lp Fourier multiplier at higher frequency as follows. It holds

    λ1eλ2tλ2eλ1tλ1λ2=eλ2t+λ2eλ2tλ1λ2λ2eλ1tλ1λ2.

    By a direct computation, it is easy to verify

    |kξλ2||ξ|2k,|ξ|η,

    which gives rise to

    |kξ[(1χ())eλ2t]|,|kξ[(1χ())λ2eλ2tλ1λ2]|{0,|ξ|R,ec1t|ξ|2k,|ξ|R,

    here and below, R>0 is a given constant. Thus, from Lemma 4.1 it follows that the inverse Fourier transform of the term (1χ())(eλ2t+λ2eλ2tλ1λ2) is an Lp multiplier with the coefficient Cec1t. The other part of ˆN2 at higher frequency can be written as

    (1χ())λ2eλ1tλ1λ2e12(2ˉμ+ˉν)|ξ|2t[(1χ())e(λ212(2ˉμ+ˉν)|ξ|2)tλ1λ2].

    We can regard e12(2ˉμ+ˉν)|ξ|2t as the function K(t,ξ) of Lemma 4.2, and the rest term satisfies the condition. Thus, the inverse Fourier transform of (1χ())λ2eλ1tλ1λ2 is also an Lp multiplier with the coefficient Cec1t. These facts imply that ˆN2 at higher frequency is an Lp multiplier with the coefficient Cec1t. Applying the similar analysis to the terms ˆN2, ˆM2, and ˆM2, we can show that their inverse Fourier transform are all Lp multiplier with the constant coefficient Cec1t. Then

    (kx(N2f),kx(N2f),kx(M2f),kx(M2f))(t)LpCec1tkxfLp, (4.9)

    for all integer k0, and p[2,].

    We also need to deal with the corresponding lower frequency terms denoted by ^()1. Recall that

    λ1eλ2tλ2eλ1tλ1λ2,λ1eλ1tλ2eλ2tλ1λ2,|ξ|(eλ1teλ2t)λ1λ2O(1)e12(2ˉμ+ˉν)|ξ|2t,|ξ|η,

    which imply that for |ξ|η that

    |ˆN1|O(1)ec2|ξ|2t,|ˆN1|O(1)ec2|ξ|2t,|ˆM1|O(1)ec2|ξ|2t,|ˆM1|O(1)ec2|ξ|2t,

    for some constants c2>0. Thus, by Hausdroff-Young's inequality with p[2,+], we can obtain

    (kN1,kN1,kM1,kM1)(t)LpC(|ξ|η||ξ|kec2|ξ|2t|qdξ)1qC(1+t)32(11p)k2. (4.10)

    Combining (4.9) and (4.10), we finally have for t>0 that

    (k(Nf),k(Mf))(t)Lp=(k((N1+N2)f),k((M1+M2)f))(t)LpC(1+t)32(11p)k2fL1+Cec1tkfLpC(1+t)32(11p)k2(fL1+kfLp).

    The proof of Proposition 4.3 is completed.

    We are ready to prove Theorem 1.1 on the sharp time decay rate of the global solution to the initial value problem for the nonlinear Navier-Stokes system.

    In what follows, we will set nh=n˜n and mh=m˜m, then we have

    {tnh+divmh=0,(t,x)R+×R3,tmh+c2nhˉμmh(ˉμ+ˉν)divmh=F,(t,x)R+×R3,lim|x|nh=0,lim|x|mh=0,tR+,(nh,mh)|t=0=(0,0),xR3, (5.1)

    where ˉμ=μˉρ, ˉν=νˉρ, c=p(ˉρ), and

    F=div{(mh+˜m)(mh+˜m)nh+˜n+ˉρ+ˉμ((nh+˜n)(mh+˜m)nh+˜n+ˉρ)}{(ˉμ+ˉν)div((nh+˜n)(mh+˜m)nh+˜n+ˉρ)+(p(nh+˜n+ˉρ)p(ˉρ)c2(nh+˜n))}.

    Denote Uh=(nh,mh)t, we have the equivalent form of system (5.1) in vector form

    tUh=BUh+H,t0,Uh(0)=0,

    where the nonlinear term H(˜U,Uh)=(0,F(˜U,Uh))t. Thus, we can represent the solution in term of the semigroup

    Uh(t)=S(t)Uh(0)+t0S(tτ)H(˜U,Uh)(τ)dτ,

    which (nh,mh) can be decomposed as

    nh=NUh(0)+t0N(tτ)H(τ)dτ, (5.2)
    mh=MUh(0)+t0M(tτ)H(τ)dτ. (5.3)

    Furthermore, in view of the above definition for ˆN(ξ) and ˆM(ξ), it is easy to verify for some constants c3>0, c4>0, R0>0, we discover that

    |ˆN(ξ)|O(1)ec3|ξ|2t,|ˆM(ξ)|O(1)ec3|ξ|2t,|ξ|η,
    |ˆN(ξ)|O(1)1|ξ|eR0t,|ˆM(ξ)|O(1)1|ξ|2eR0t+O(1)ec4|ξ|2t,|ξ|η.

    Thus, applying a similar argument as in the proof of Proposition 4.1, we have

    (kNH,kMH)(t)L2C(1+t)32(1q12)12k2(QLq+k+1QL2),q=1,2, (5.4)
    (kNH,kMH)(t)L2C(1+t)32(1q12)12k2(QLq+kQL2),q=1,2, (5.5)
    kMH(t)L2C(1+t)32(1q12)12k2(QLq+k1QL2),q=1,2, (5.6)

    for any non-negative integer k and

    Q=|(mh+˜m)(mh+˜m)nh+˜n+ˉρ+ˉμ((nh+˜n)(mh+˜m)nh+˜n+ˉρ)|+|(ˉμ+ˉν)div((nh+˜n)(mh+˜m)nh+˜n+ˉρ)+(p(nh+˜n+ˉρ)p(ˉρ)c2(nh+˜n))|. (5.7)

    For readers' convenience, we show how to estimate kMH(t)L2 as an example. The other two estimates can be obtained by the similar argument. Indeed,

    kMH(t)2L2|ξ|ηe2c3|ξ|2t|ξ|2k|ˆH|2dξ+|ξ|ηe2R0t|ξ|2k4|ˆH|2dξ+|ξ|ηe2c4|ξ|2t|ξ|2k|ˆH|2dξ|ξ|ηe2c3|ξ|2t|ξ|2k+2|ˆQ|2dξ+|ξ|ηe2R0t|ξ|2k2|ˆQ|2dξ+|ξ|ηe2c4|ξ|2t|ξ|2k+2|ˆQ|2dξ(1+t)3(1q12)1k(Q2Lq(R3)+˜kQ2L2(R3)),q=1,2,k1˜kN+.

    In this subsection, we establish the faster decay rate for (nh,mh). We will start with an a priori assumption on a carefully chosen quantity Λ(t) defined in (5.8), and then later prove a better estimate with the help of the smallness of initial data.

    We begin with following Lemma.

    Lemma 5.1. Let r1,r2>0 be real, one has

    t20(1+tτ)r1(1+τ)r2dτ=t20(1+t2+τ)r1(1+t2τ)r2dτ{(1+t)r1,forr2>1,(1+t)(r1ϵ),forr2=1,(1+t)(r1+r21),forr2<1,

    and

    tt2(1+tτ)r1(1+τ)r2dτ=t20(1+tτ)r2(1+τ)r1dτ{(1+t)r2,forr1>1,(1+t)(r2ϵ),forr1=1,(1+t)(r1+r21),forr1<1,

    where ϵ>0 is a small but fixed constant.

    Proposition 5.1. Under the assumptions of Theorem 1.1, the solution (nh,mh) of the nonlinear system (5.1) satisfies for k=0,1,2 that

    (knh,kmh)L2Cδ20(1+t)54k2,3mhL2Cδ20(1+t)114,3nhL2Cδ0(1+t)74,

    where C is a positive constant independent of time.

    From (5.7), we deduce

    Q(˜U,Uh)=Q1+Q2+Q3+Q4,

    which implies for a smooth solution (n,m) satisfying (n,m)H3< that

    Q1=Q1(˜U,Uh)O(1)(n2h+mhmh+˜n2+˜m˜m),Q2=Q2(˜U,Uh)O(1)(˜nnh+˜mmh),Q3=Q3(˜U,Uh)O(1)((nhmh)+(˜n˜m)),Q4=Q4(˜U,Uh)O(1)((˜nmh)+(nh˜m)).

    Define

    Λ(t)=:sup0st{2k=0(1+s)54+k2δ034(knh,kmh)(s)L2+(1+s)74(3nh,3mh)(s)L2}. (5.8)

    Proposition 5.2. Under the assumptions of Theorem 1.1, if for some T>0, Λ(t)δ120 for any t[0,T], then it holds that

    Λ(t)Cδ340,t[0,T],

    where C is a positive constant independent of time.

    The proof of this Proposition 5.2 consists of following three steps.

    Starting with (5.4), (5.5), (5.6) and (5.8), we have after a complicate but straightforward computation that

    (nh,mh)L2t0(N(tτ)H(τ),M(tτ)H(τ))L2dτt0(1+tτ)54(Q(τ)L1+Q(τ)L2)dτ(δ20+δ320Λ2(t))t0(1+tτ)54(1+τ)32dτ(1+t)54(δ20+δ320Λ2(t)). (5.9)

    It is easy to verify that

    Q(t)L1Q1L1+Q2L1+Q3L1+Q4L1(˜n,˜m)2L2+(nh,mh)2L2+(nh,mh)L2((˜n,˜m)L2+(nh,mh)L2)+(˜n,˜m)L2((˜n,˜m)L2+(nh,mh)L2)(1+t)32(δ20+δ320Λ2(t)).

    Indeed, by virtue of Hölder's inequality and Gagliardo-Nirenberg's inequality, we obtain that

    uLu12L22u12L2,

    which implies that

    Q(t)L2(˜n,˜m)L((˜n,˜m)L2+(˜n,˜m)L2+(nh,mh)L2+(nh,mh)L2)+(nh,mh)L((nh,mh)L2+(nh,mh)L2)+(˜n,˜m)L(nh,mh)L2(1+t)94(δ20+δ320Λ2(t)).

    Furthermore, exactly as in the estimate of the high order derivatives, we have

    (nh,mh)L2t20(N,M)(tτ)H(τ)L2dτ+tt2(N,M)(tτ)H(τ)L2dτt20(1+tτ)74(Q(τ)L1+Q(τ)L2)dτ+tt2(1+tτ)12Q(τ)L2dτ(δ20+δ980Λ2(t))(t20(1+tτ)74(1+τ)32dτ+tt2(1+tτ)12(1+τ)114dτ)(1+t)74(δ20+δ980Λ2(t)), (5.10)

    Similarly, it holds that

    Q(t)L2(˜n,˜m)L((˜n,˜m)L2+(2˜n,2˜m)L2+(nh,mh)L2
    +(2nh,2mh)L2)+(˜n,˜m)L((˜n,˜m)L2+(nh,mh)L2+(nh,mh)L2)+(nh,mh)L((2˜n,2˜m)L2+(nh,mh)L2+(2nh,2mh)L2)+(nh,mh)L(nh,mh)L2(1+t)114(δ20+δ980Λ2(t)).

    Thus, we also get that

    (2nh,2mh)(t)L2t20(2N,2M)(tτ)H(τ)L2dτ+tt2(N,M)(tτ)2H(τ)L2dτt20(1+tτ)94(Q(τ)L1+2Q(τ)L2)dτ+tt2(1+tτ)122Q(τ)L2dτ(δ20+δ0Λ(t)+δ340Λ2(t))(t20(1+tτ)94(1+τ)32dτ+tt2(1+tτ)12(1+τ)134dτ)(1+t)94(δ20+δ0Λ(t)+δ340Λ2(t)). (5.11)

    Finally, we have

    2Q(t)L2((˜n,˜m)L+(nh,mh)L)((3˜n,3˜m)L2+(3nh,3mh)L2)+((˜n,˜m)L+(nh,mh)L)((˜n,˜m)L2+(nh,mh)L2)+((˜n,˜m)L+(nh,mh)L+(˜n,˜m)L+(nh,mh)L)×((2˜n,2˜m)L2+(2nh,2mh)L2)(1+t)134(δ20+δ0Λ(t)+δ340Λ2(t)).

    In this subsection, we will close the a priori estimates and complete the proof of Proposition 5.2. For this purpose, we need to derive the time decay rate of higher order derivatives of (nh,mh). We will establish the following lemma.

    Lemma 5.2. Under the assumption of Theorem 1.1, one has

    2n(t)H1+2u(t)H1(1+t)74(δ0+δ340Λ(t)).

    In particular, it holds that

    \begin{eqnarray*} \|{\nabla}^3 (n_h, m_h)(t)\|_{L^2}\lesssim (1+t)^{-\frac74}\left(\delta_0+\delta_0^{\frac34}\Lambda(t)\right). \end{eqnarray*}

    Proof. First of all, in view of (2.12), recovering the dissipation estimate for n , we see that

    \begin{equation} \begin{split} &\frac{d}{dt}\int_{{\mathop{\mathbb R\kern 0pt}\nolimits}^3} {\nabla}^2 u\cdot {\nabla}^3 n dx +C_1\|{\nabla}^3 n\|_{L^2}^2 dx\\ \leq &C_2\left(\|{\nabla}^3 u\|_{L^2}^2+\|{\nabla}^4 u\|_{L^2}^2\right)+C(1+t)^{-\frac{3}2}\left(\delta_0+\delta_0^{\frac38}\Lambda(t)\right)\\ &\quad\times\left(\|{\nabla}^2 n\|_{L^2}^2+\|{\nabla}^2 u\|_{L^2}^2+\|{\nabla}^3 u\|_{L^2}^2\right). \end{split} \end{equation} (5.12)

    Summing up (2.7) and (2.8) in the energy estimate for (n,u) , we can directly derive

    \begin{equation} \begin{split} &\frac{d}{dt}\int_{{\mathop{\mathbb R\kern 0pt}\nolimits}^3} \left(\gamma |{\nabla}^2 n|^2+|{\nabla}^2 u|^2 + \gamma|{\nabla}^3 n|^2 +|{\nabla}^3 u|^2 \right)dx + C_3\left(\|{\nabla}^3 u|^2 _{L^2}+\|{\nabla}^4 u\|^2 _{L^2}\right) \\ \leq &C(1+t)^{-\frac{3}2}\left(\delta_0+\delta_0^{\frac38}\Lambda(t)\right)\left(\|{\nabla}^2 n\|_{L^2}^2+\|{\nabla}^2 u\|_{L^2}^2+\|{\nabla}^3 n\|_{L^2}^2\right). \end{split} \end{equation} (5.13)

    Multiplying (5.12) by \epsilon_1\frac{C_3}{C_2} with \epsilon_1>0 a small but fixed constant, adding it with (5.13), we deduce that there exists a constant C_4>0 such that

    \begin{eqnarray*} \begin{split} &\frac{d}{dt}\bigg\{\sum\limits_{2\leq k\leq3}\left(\gamma \|{\nabla}^k n\|^2_{L^2}+\|{\nabla}^k u\|^2_{L^2} \right)+\epsilon_1\frac{C_3}{C_2}\int_{{\mathop{\mathbb R\kern 0pt}\nolimits}^3} {\nabla}^2 u\cdot {\nabla}^{3} n dx \bigg\}\\ &\quad+ C_4\Big(\|{\nabla}^{3} n\|_{L^2}^2+\sum\limits_{3\leq k\leq4}\|{\nabla}^{k} u\|^2_{L^2}\Big)\\ \leq &C(1+t)^{-\frac{3}2}\left(\delta_0+\delta_0^{\frac38}\Lambda(t)\right)\left(\|{\nabla}^2 n\|_{L^2}^2+\|{\nabla}^2 u\|_{L^2}^2\right). \end{split} \end{eqnarray*}

    Next, we define

    \mathcal E_1(t) = \bigg\{\sum\limits_{2\leq k\leq3}\left(\gamma \|{\nabla}^k n\|^2_{L^2}+\|{\nabla}^k u\|^2_{L^2} \right)+\epsilon_1\frac{C_3}{C_2}\int_{{\mathop{\mathbb R\kern 0pt}\nolimits}^3} {\nabla}^2 u\cdot {\nabla}^{3} n dx \bigg\}.

    Observe that since \epsilon_1\frac{C_3}{C_2} is small, then there exists a constant C_5>0 such that

    \begin{eqnarray*} C_5^{-1}\left(\|{\nabla}^2 n(t)\|^2_{H^1}+\|{\nabla}^2 u(t)\|^2_{H^1}\right) \leq\mathcal E_1(t)\leq C_5\left(\|{\nabla}^2 n(t)\|^2_{H^1}+\|{\nabla}^2 u(t)\|^2_{H^1}\right). \end{eqnarray*}

    Then we arrive at

    \begin{eqnarray*} \frac{d}{dt}\mathcal E_1(t)+C_4\Big(\|{\nabla}^{3} n(t)\|_{L^2}^2+\|{\nabla}^3 u(t)\|^2_{H^1}\Big) \leq C(1+t)^{-5}\left(\delta_0+\delta_0^{\frac38}\Lambda(t)\right)\left(\delta_0^2+\delta_0^{\frac32}\Lambda^2(t)\right). \end{eqnarray*}

    Denote S(t) = \Big\{\xi\big| |\xi| \leq \sqrt{\frac{3(1+\gamma)}{C_4}}(1+t)^{-\frac12}\Big\} the time-dependent n -dimensional sphere. This decomposition allows us to estimate L^2 time decay depend on (\widehat {n}, \widehat {u}) for frequency values \xi \in S(t) , then we obtain that

    \begin{eqnarray*} \begin{split} &\frac{C_4}{3}\|{\nabla}^{3} (n, u)(x)\|_{L^2}^2 \geq\frac{C_4}{3}\int_{S(t)^c} |\xi|^6|(\widehat{n}, \widehat{u})(\xi)|^2d\xi\\ \geq&(1+\gamma)(1+t)^{-1}\int_{{\mathop{\mathbb R\kern 0pt}\nolimits}^3} |\xi|^4|(\widehat{n}, \widehat{u})(\xi)|^2d\xi-(1+\gamma)(1+t)^{-1}\int_{S(t)} |\xi|^4|(\widehat{n}, \widehat{u})(\xi)|^2d\xi. \end{split} \end{eqnarray*}

    Hence we have

    \begin{eqnarray*} \begin{split} &\frac{d}{dt}\mathcal E_1(t)+(1+t)^{-1}\mathcal E_1(t)+\|{\nabla}^{3} n\|_{L^2}^2+\|{\nabla}^3 u\|^2_{H^1}\\ \lesssim&(1+t)^{-5}\left(\delta_0+\delta_0^{\frac38}\Lambda(t)\right)\left(\delta_0^2+\delta_0^{\frac32}\Lambda^2(t)\right)+(1+t)^{-1}\int_{S(t)} |\xi|^4|(\widehat{n}, \widehat{u})(\xi)|^2d\xi\\ &\quad+(1+t)^{-1}\int_{{\mathop{\mathbb R\kern 0pt}\nolimits}^3} {\nabla}^2 u\cdot {\nabla}^{3} n dx. \end{split} \end{eqnarray*}

    Multiplying the above equation by (1+t)^5 , we obtain that

    \begin{eqnarray*} \begin{split} &\frac{d}{dt}\Big\{(1+t)^5\mathcal E_1(t)\Big\}+(1+t)^5\Big(\|{\nabla}^{3} n\|_{L^2}^2+\|{\nabla}^3 u\|^2_{H^1}\Big) \lesssim(1+t)^{\frac12}\left(\delta_0^2+\delta_0^{\frac32}\Lambda^2(t)\right). \end{split} \end{eqnarray*}

    Integrating it with respect to time from 0 to T , then we have

    \begin{eqnarray*} \begin{split} &(1+t)^5\mathcal E_1(t)+\int_0^T(1+t)^5\Big(\|{\nabla}^{3} n\|_{L^2}^2+\|{\nabla}^3 u\|^2_{H^1}\Big)dt\\ \lesssim& \mathcal E_1(0)+(1+t)^{\frac32}\left(\delta_0^2+\delta_0^{\frac32}\Lambda^2(t)\right), \end{split} \end{eqnarray*}

    which implies that

    \begin{eqnarray*} \|{\nabla}^3 n\|^2_{L^2}+\|{\nabla}^3 u\|^2_{L^2}\lesssim\mathcal E_1(t)\lesssim (1+t)^{-5}\delta_0^2+(1+t)^{-\frac72}\left(\delta_0^2+\delta_0^{\frac32}\Lambda^2(t)\right). \end{eqnarray*}

    Finally, we have

    \begin{eqnarray*} \|{\nabla}^3 n_h\|_{L^2}+\|{\nabla}^3 m_h\|_{L^2}\lesssim (1+t)^{-\frac74}\left(\delta_0+\delta_0^{\frac34}\Lambda(t)\right). \end{eqnarray*}

    This completes the proof of this Lemma.

    In this subsection, we first combine the above a priori estimates of (5.8), (5.9), (5.10), (5.11) and Lemma 5.2 together to give the proof of the Proposition 5.2. In deed, for any t\in[0,T] , we have shown that

    \begin{equation} \Lambda(t)\leq C\left(\delta_0+\delta_0^{\frac14}\Lambda(t)+\Lambda^2(t)\right) \leq C\delta_0^{\frac34}. \end{equation} (5.14)

    With the help of standard continuity argument, Proposition 5.2 and the smallness of \delta_0>0 , implies that \Lambda(t)\leq C\delta_0^{\frac34} for any t>0 . Moreover, we deduce the time decay estimate for (n_h, m_h) from (5.9), (5.10), (5.11), Lemma 5.2 and (5.14) that

    \begin{eqnarray*} \begin{split} &\|({\nabla}^k n_h, {\nabla}^k m_h)\|_{L^2}\lesssim \delta_0^2(1+t)^{-\frac54-\frac k2},\quad k = 0,1,\\ &\|{\nabla}^2 (n_h, m_h)\|_{L^2}\lesssim\delta_0^{\frac74}(1+t)^{-\frac94},\quad \|{\nabla}^3 (n_h, m_h)\|_{L^2}\lesssim\delta_0(1+t)^{-\frac74}. \end{split} \end{eqnarray*}

    Consequently, for any t\in[0,T] we have

    \begin{equation} \Lambda(t)\leq C\delta_0. \end{equation} (5.15)

    From (5.11) and (5.15), thus we also get that

    \begin{eqnarray*} \|{\nabla}^2 (n_h, m_h)\|_{L^2}\lesssim\delta_0^2(1+t)^{-\frac94}. \end{eqnarray*}

    For {\nabla}^3 m_h , in view of the (5.6), we see that

    \begin{eqnarray*} \begin{split} &\|{\nabla}^3 m_h(t)\|_{L^2} \\\lesssim &\int_0^{\frac t 2}(1+t-\tau)^{-\frac{11}4}\big(\|Q(\tau)\|_{L^1}+\|{\nabla}^2 Q(\tau)\|_{L^2}\big)d\tau\\ &\quad+\int_{\frac t 2}^t (1+t-\tau)^{-\frac12}\|{\nabla} ^2Q(\tau)\|_{L^2}d\tau\\ \lesssim& \delta_0^2\bigg(\int_0^{\frac t 2} (1+t-\tau)^{-\frac{11}4}(1+\tau)^{-\frac32}d\tau+\int_{\frac t 2}^t (1+t-\tau)^{-\frac12}(1+\tau)^{-\frac{13}4}d\tau\bigg)\\ \lesssim&\delta_0^2(1+t)^{-\frac{11}4}. \end{split} \end{eqnarray*}

    Hence, we finish the proof of the Proposition 5.1. Theorem 1.1 follows.

    Y. Chen is partially supported by the China Postdoctoral Science Foundation under grant 2019M663198, Guangdong Basic and Applied Basic Research Foundation under grant 2019A1515110733, NNSF of China under grants 11801586, 11971496 and China Scholarship Council. The research of R. Pan is partially supported by National Science Foundation under grants DMS-1516415 and DMS-1813603, and by National Natural Science Foundation of China under grant 11628103. L. Tong's research is partially supported by China Scholarship Council.



    [1] J. B. Hiriart-Urruty, W. Oettli, J. Stoer, Optimization: Theory and Algorithms, CRC Press, 2020. https://doi.org/10.1201/9781003065098
    [2] M. Tyagi, A. Sachdeva, V. Sharma, Optimization Methods in Engineering, Springer, 2021. https://doi.org/10.1007/978-981-15-4550-4
    [3] P. Adby, Introduction to Optimization Methods, Springer Science & Business Media, 2013.
    [4] A. P. Engelbrecht, Computational Intelligence: An Introduction, John Wiley & Sons, 2007. https://doi.org/10.1002/9780470512517
    [5] J. S. Raj, A comprehensive survey on the computational intelligence techniques and its applications, J. ISMAC, 01 (2019), 147–159. https://doi.org/10.36548/jismac.2019.3.002 doi: 10.36548/jismac.2019.3.002
    [6] K. Hussain, M. N. Mohd Salleh, S. Cheng, Y. Shi, Metaheuristic research: a comprehensive survey, Artif. Intell. Rev., 52 (2019), 2191–2233. https://doi.org/10.1007/s10462-017-9605-z doi: 10.1007/s10462-017-9605-z
    [7] S. Yin, Q. Luo, Y. Zhou, EOSMA: An equilibrium optimizer slime mould algorithm for engineering design problems, Arab. J. Sci. Eng., 47 (2022), 10115–10146. https://doi.org/10.1007/s13369-021-06513-7 doi: 10.1007/s13369-021-06513-7
    [8] Y. Zhang, Y. Zhou, G. Zhou, Q. Luo, B. Zhu, A curve approximation approach using bio-inspired polar coordinate bald eagle search algorithm, Int. J. Comput. Intell. Sys., 15 (2022), 30. https://doi.org/10.1007/s44196-022-00084-7 doi: 10.1007/s44196-022-00084-7
    [9] N. Du, Y. Zhou, W. Deng, Q. Luo, Improved chimp optimization algorithm for three-dimensional path planning problem, Mul. Tools Appl., 81 (2022), 27397–27422. https://doi.org/10.1007/s11042-022-12882-4 doi: 10.1007/s11042-022-12882-4
    [10] M. Kumar, M. Husain, N. Upreti, D. Gupta, Genetic algorithm: Review and application, J. SSRN Elec., 2010 (2010). https://doi.org/10.2139/ssrn.3529843 doi: 10.2139/ssrn.3529843
    [11] R. Storn, K. Price, Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341–359. https://doi.org/10.1023/A:1008202821328 doi: 10.1023/A:1008202821328
    [12] J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95International Conference on Neural Networks, (1995), 1942–1948.
    [13] M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. Part B, 26 (1996), 29–41. https://doi.org/10.1109/3477.484436 doi: 10.1109/3477.484436
    [14] S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
    [15] X. S. Yang, S. Deb, Cuckoo search: recent advances and applications, Neural Comput. Appl., 24 (2014), 169–174. https://doi.org/10.1007/s00521-013-1367-1 doi: 10.1007/s00521-013-1367-1
    [16] S. Li, H. Chen, M. Wang, A. A. Heidari, S. Mirjalili, Slime mould algorithm: A new method for stochastic optimization, Future Gener. Comput. Syst., 111 (2020), 300–323. https://doi.org/10.1016/j.future.2020.03.055 doi: 10.1016/j.future.2020.03.055
    [17] A. Faramarzi, M. Heidarinejad, S. Mirjalili, A. H. Gandomi, Marine predators algorithm: A nature-inspired metaheuristic, Exp. Syst. Appl., 152 (2020), 113377. https://doi.org/10.1016/j.eswa.2020.113377 doi: 10.1016/j.eswa.2020.113377
    [18] B. Abdollahzadeh, F. S. Gharehchopogh, S. Mirjalili, African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Comput. Ind. Eng., 158 (2021), 107408. https://doi.org/10.1016/j.cie.2021.107408 doi: 10.1016/j.cie.2021.107408
    [19] H. A. Alsattar, A. A. Zaidan, B. B. Zaidan, Novel meta-heuristic bald eagle search optimisation algorithm, Artif. Intell. Rev., 53 (2020), 2237–2264. https://doi.org/10.1007/s10462-019-09732-5 doi: 10.1007/s10462-019-09732-5
    [20] D. Huang, X. Zhu, A novel method based on chemical reaction optimization for pairwise sequence alignment, in Parallel Computational Fluid Dynamics, Springer Berlin Heidelberg, (2014), 429–439. https://doi.org/10.1007/978-3-642-53962-6_38
    [21] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems, Appl. Intell., 51 (2021), 1531–1551. https://doi.org/10.1007/s10489-020-01893-z doi: 10.1007/s10489-020-01893-z
    [22] A. Rabehi, B. Nail, H. Helal, A. Douara, A. Ziane, M. Amrani, et al., Optimal estimation of Schottky diode parameters using a novel optimization algorithm: Equilibrium optimizer, Superlattices Microstruct., 146 (2020), 106665. https://doi.org/10.1016/j.spmi.2020.106665 doi: 10.1016/j.spmi.2020.106665
    [23] R. V. Rao, V. J. Savsani, D. P. Vakharia, Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput. Aided Des., 43 (2011), 303–315. https://doi.org/10.1016/j.cad.2010.12.015 doi: 10.1016/j.cad.2010.12.015
    [24] A. W. Mohamed, A. A. Hadi, A. K. Mohamed, Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm, Int. J. Mach. Learn. Cybern., 11 (2020), 1501–1529. https://doi.org/10.1007/s13042-019-01053-x doi: 10.1007/s13042-019-01053-x
    [25] Y. B. Chen, Y. S., Mei, J. Q. Yu, X. L. Su, N. Xu, Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm, Neurocomputing, 266 (2017), 445–457. https://doi.org/10.1016/j.neucom.2017.05.059 doi: 10.1016/j.neucom.2017.05.059
    [26] H. Duan, Y. Yu, X. Zhang, S. Shao, Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm, Simul. Modell. Pract. Theory, 18 (2010), 1104–1115. https://doi.org/10.1016/j.simpat.2009.10.006 doi: 10.1016/j.simpat.2009.10.006
    [27] P. Saxena, S. Tayal, R. Gupta, A. Maheshwari, G. Kaushal, R. Tiwari, Three dimensional route planning for multiple unmanned aerial vehicles using salp swarm algorithm, J. Exp. Theor. Artif. Intell., 35 (2023), 1059–1078. https://doi.org/10.1080/0952813X.2022.2059107 doi: 10.1080/0952813X.2022.2059107
    [28] U. Goel, S. Varshney, A. Jain, S. Maheshwari, A. Shukla, Three dimensional path planning for UAVs in dynamic environment using glow-worm swarm optimization, Proc. Comput. Sci., 133 (2018), 230–239. https://doi.org/10.1016/j.procs.2018.07.028 doi: 10.1016/j.procs.2018.07.028
    [29] Y. Zhang, Y. Zhou, G. Zhou, Q. Luo, An effective multi-objective bald eagle search algorithm for solving engineering design problems, Appl. Soft Comput., 145 (2023), 110585. https://doi.org/10.1016/j.asoc.2023.110585 doi: 10.1016/j.asoc.2023.110585
    [30] S. Yin, Q. Luo, Y. Zhou, IBMSMA: An indicator-based multi-swarm slime mould algorithm for multi-objective truss optimization problems, J. Bionic Eng., 20 (2023), 1333–1360. https://doi.org/10.1007/s42235-022-00307-9 doi: 10.1007/s42235-022-00307-9
    [31] G. I. Sayed, M. M. Soliman, A. E. Hassanien, A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization, Comput. Bio. Med., 136 (2021), 104712. https://doi.org/10.1016/j.compbiomed.2021.104712 doi: 10.1016/j.compbiomed.2021.104712
    [32] H. A. Almashhadani, X. Deng, S. N. A. Latif, M. M. Ibrahim, O. H. R. Al-hwaidi, Deploying an efficient and reliable scheduling for mobile edge computing for IoT applications, Mater. Today Proc., 80 (2023), 2850–2857. https://doi.org/10.1016/j.matpr.2021.07.050 doi: 10.1016/j.matpr.2021.07.050
    [33] A. M. Nassef, A. Fathy, H. Rezk, D. Yousri, Optimal parameter identification of supercapacitor model using bald eagle search optimization algorithm, J. Energy Storage, 50 (2022), 104603. https://doi.org/10.1016/j.est.2022.104603 doi: 10.1016/j.est.2022.104603
    [34] A. D. Algarni, N. Alturki, N. F. Soliman, S. Abdel-Khalek, A. A. A. Mousa, An improved bald eagle search algorithm with deep learning model for forest fire detection using hyperspectral remote sensing images, Can. J. Remote Sens, 48 (2022), 609–620. https://doi.org/10.1080/07038992.2022.2077709 doi: 10.1080/07038992.2022.2077709
    [35] A. Eid, S. Kamel, H. M. Zawbaa, M. Dardeer, Improvement of active distribution systems with high penetration capacities of shunt reactive compensators and distributed generators using bald eagle search, Ain Shams Eng. J., 13 (2022), 101792. https://doi.org/10.1016/j.asej.2022.101792 doi: 10.1016/j.asej.2022.101792
    [36] S. Alsubai, M. Hamdi, S. Abdel-Khalek, A. Alqahtani, A. Binbusayyis, R. F. Mansour, Bald eagle search optimization with deep transfer learning enabled age-invariant face recognition model, Image Vis. Comput., 126 (2022), 104545. https://doi.org/10.1016/j.imavis.2022.104545 doi: 10.1016/j.imavis.2022.104545
    [37] M. Elsisi, M. E. S. M. Essa, Improved bald eagle search algorithm with dimension learning-based hunting for autonomous vehicle including vision dynamics, Appl. Intell., 53 (2023), 11997–12014. https://doi.org/10.1007/s10489-022-04059-1 doi: 10.1007/s10489-022-04059-1
    [38] Y. Chen, W. Wu, P. Jiang, C. Wan, An improved bald eagle search algorithm for global path planning of unmanned vessel in complicated waterways, J. Mar. Sci. Eng., 11 (2023), 118. https://doi.org/10.3390/jmse11010118 doi: 10.3390/jmse11010118
    [39] S. Dian, J. Zhong, B. Guo, J. Liu, R. Guo, A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm, Expert Syst. Appl., 208 (2022), 118256. https://doi.org/10.1016/j.eswa.2022.118256 doi: 10.1016/j.eswa.2022.118256
    [40] Y. Niu, X. Yan, Y. Wang, Y. Niu, Three-dimensional collaborative path planning for multiple UCAVs based on improved artificial ecosystem optimizer and reinforcement learning, Knowl. Based Syst., 276 (2023), 110782. https://doi.org/10.1016/j.knosys.2023.110782 doi: 10.1016/j.knosys.2023.110782
    [41] G. Hu, B. Du, G. Wei, HG-SMA: hierarchical guided slime mould algorithm for smooth path planning, Artif. Intell. Rev., 56 (2023), 9267–9327. https://doi.org/10.1007/s10462-023-10398-3 doi: 10.1007/s10462-023-10398-3
    [42] D. Agarwal, P. S. Bharti, Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots, Appl. Soft Comput., 107 (2021), 107372. https://doi.org/10.1016/j.asoc.2021.107372 doi: 10.1016/j.asoc.2021.107372
    [43] Y. Cui, W. Hu, A. Rahmani, Multi-robot path planning using learning-based artificial bee colony algorithm, Eng. Appl. Artif. Intell., 129 (2024), 107579. https://doi.org/10.1016/j.engappai.2023.107579 doi: 10.1016/j.engappai.2023.107579
    [44] C. Miao, G. Chen, C. Yan, Y. Wu, Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm, Comput. Ind. Eng., 156 (2021), 107230. https://doi.org/10.1016/j.cie.2021.107230 doi: 10.1016/j.cie.2021.107230
    [45] X. Yu, C. Li, J. Zhou, A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios, Knowl. Based Syst., 204 (2020), 106209. https://doi.org/10.1016/j.knosys.2020.106209 doi: 10.1016/j.knosys.2020.106209
    [46] R. Wang, M. Lungu, Z. Zhou, X. Zhu, Y. Ding, Q. Zhao, Least global position information based control of fixed-wing UAVs formation flight: Flight tests and experimental validation, Aerosp. Sci. Technol., 140 (2023), 108473. https://doi.org/10.1016/j.ast.2023.108473 doi: 10.1016/j.ast.2023.108473
    [47] P. C. Song, J. S. Pan, S. C. Chu, A parallel compact cuckoo search algorithm for three-dimensional path planning, Appl. Soft Comput., 94 (2020), 106443. https://doi.org/10.1016/j.asoc.2020.106443 doi: 10.1016/j.asoc.2020.106443
    [48] T. Ren, R. Zhou, J. Xia, Z. Dong, Three-dimensional path planning of UAV based on an improved A* algorithm, in 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), (2016), 140–145. https://doi.org/10.1109/CGNCC.2016.7828772
    [49] H. Daryanavard, A. Harifi, UAV path planning for data gathering of IoT nodes: ant colony or simulated annealing optimization, in 2019 3rd International Conference on Internet of Things and Applications (IoT), (2019), 1–4. https://doi.org/10.1109/IICITA.2019.8808834
    [50] Q. Wang, A. Zhang, L. Qi, Three-dimensional path planning for UAV based on improved PSO algorithm, in the 26th Chinese Control and Decision Conference (2014 CCDC), (2014), 3981–3985. https://doi.org/10.1109/CCDC.2014.6852877
    [51] C. Qu, W. Gai, J. Zhang, M. Zhong, A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning, Knowl. Based Syst., 194 (2020), 105530. https://doi.org/10.1016/j.knosys.2020.105530 doi: 10.1016/j.knosys.2020.105530
    [52] U. Cekmez, M. Ozsiginan, O. K. Sahingoz, A UAV path planning with parallel ACO algorithm on CUDA platform, in 2014 International Conference on Unmanned Aircraft Systems (ICUAS), (2014), 347–354. https://doi.org/10.1109/ICUAS.2014.6842273
    [53] S. Ghambari, L. Idoumghar, L. Jourdan, J. Lepagnot, An improved TLBO algorithm for solving UAV path planning problem, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, (2019), 2261–2268. https://doi.org/10.1109/SSCI44817.2019.9003160
    [54] S. Ghambari, J. Lepagnot, L. Jourdan, L. Idoumghar, A comparative study of meta-heuristic algorithms for solving UAV path planning, in 2018 IEEE Symposium Series on Computational Intelligence (SSCI), (2018), 174–181. https://doi.org/10.1109/SSCI.2018.8628807
    [55] S. Zhang, Y. Zhou, Z. Li, W. Pan, Grey wolf optimizer for unmanned combat aerial vehicle path planning, Adv. Eng. Software, 99 (2016), 121–136. https://doi.org/10.1016/j.advengsoft.2016.05.015 doi: 10.1016/j.advengsoft.2016.05.015
    [56] S. Yin, Q. Luo, Y. Du, Y. Zhou, DTSMA: Dominant swarm with adaptive T-distribution mutation-based slime mould algorithm, Math. Biosci. Eng., 19 (2022), 2240–2285. https://doi.org/10.3934/mbe.2022105 doi: 10.3934/mbe.2022105
    [57] C. J. M. Moctezuma, J. Mora, M. G. Mendoza, A self-adaptive mechanism using weibull probability distribution to improve metaheuristic algorithms to solve combinatorial optimization problems in dynamic environments, Math. Biosci. Eng., 17 (2020), 975–997. https://doi.org/10.3934/mbe.2020052 doi: 10.3934/mbe.2020052
    [58] G. Zhou, Y. Zhou, W. Deng, S. Yin, Y. Zhang, Advances in teaching–learning-based optimization algorithm: A comprehensive survey (ICIC2022), Neurocomputing, 561 (2023), 126898. https://doi.org/10.1016/j.neucom.2023.126898 doi: 10.1016/j.neucom.2023.126898
    [59] A. E. Ezugwu, A. M. Ikotun, O. O. Oyelade, L. Abualigah, J. O. Agushaka, C. I. Eke, et al., A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects, Eng. Appl. Artif. Intell., 110 (2022), 104743. https://doi.org/10.1016/j.engappai.2022.104743 doi: 10.1016/j.engappai.2022.104743
    [60] P. Singh, N. Mittal, An efficient localization approach to locate sensor nodes in 3D wireless sensor networks using adaptive flower pollination algorithm, Wireless Networks, 27 (2021), 1999–2014. https://doi.org/10.1007/s11276-021-02557-7 doi: 10.1007/s11276-021-02557-7
    [61] K. Hu, L. Wang, J. Cai, L. Cheng, An improved genetic algorithm with dynamic neighborhood search for job shop scheduling problem, Math. Biosci. Eng., 20 (2023), 17407–17427. https://doi.org/10.3934/mbe.2023774 doi: 10.3934/mbe.2023774
    [62] T. Zhang, Y. Zhou, G. Zhou, W. Deng, Q. Luo, Discrete Mayfly Algorithm for spherical asymmetric traveling salesman problem, Exp. Syst. Appl., 221 (2023), 119765. https://doi.org/10.1016/j.eswa.2023.119765 doi: 10.1016/j.eswa.2023.119765
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