
Evolutionary multitasking algorithms aim to solve several optimization tasks simultaneously, and they can improve the efficiency of various tasks evolution through the knowledge transfer between different optimization tasks. Evolutionary multitasking algorithms have been applied to various applications and achieved certain results. However, how to transfer knowledge between tasks is still a problem worthy of research. Aiming to improve the positive transfer between tasks and reduce the negative transfer, we propose a single-objective multitask optimization algorithm based on elite individual transfer, namely MSOET. In this paper, whether to execute knowledge transfer between tasks depends on a certain probability. Meanwhile, in order to enhance the effectiveness and the global search ability of the algorithm, the current population and the elite individual in the transfer population are further utilized as the learning sources to construct a Gaussian distribution model, and the offspring is generated by the Gaussian distribution model to achieve knowledge transfer between tasks. We compared the proposed MSOET with ten multitask optimization algorithms, and the experimental results verify the algorithm's excellent performance and strong robustness.
Citation: Yutao Lai, Hongyan Chen, Fangqing Gu. A multitask optimization algorithm based on elite individual transfer[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 8261-8278. doi: 10.3934/mbe.2023360
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Evolutionary multitasking algorithms aim to solve several optimization tasks simultaneously, and they can improve the efficiency of various tasks evolution through the knowledge transfer between different optimization tasks. Evolutionary multitasking algorithms have been applied to various applications and achieved certain results. However, how to transfer knowledge between tasks is still a problem worthy of research. Aiming to improve the positive transfer between tasks and reduce the negative transfer, we propose a single-objective multitask optimization algorithm based on elite individual transfer, namely MSOET. In this paper, whether to execute knowledge transfer between tasks depends on a certain probability. Meanwhile, in order to enhance the effectiveness and the global search ability of the algorithm, the current population and the elite individual in the transfer population are further utilized as the learning sources to construct a Gaussian distribution model, and the offspring is generated by the Gaussian distribution model to achieve knowledge transfer between tasks. We compared the proposed MSOET with ten multitask optimization algorithms, and the experimental results verify the algorithm's excellent performance and strong robustness.
Nonlinear partial differential equation is a very important branch of the nonlinear science, which has been called the foreword and hot topic of current scientific development. In theoretical science and practical application, nonlinear partial differential is used to describe the problems in the fields of optics, mechanics, communication, control science and biology [1,2,3,4,5,6,7,8,9]. At present, the main problems in the study of nonlinear partial differential equations are the existence of solutions, the stability of solutions, numerical solutions and exact solutions. With the development of research, especially the study of exact solutions of nonlinear partial differential equations has important theoretical value and application value. In the last half century, many important methods for constructing exact solutions of nonlinear partial differential equations have been proposed, such as the planar dynamic system method [10], the Jacobi elliptic function method [11], the bilinear transformation method [12], the complete discriminant system method for polynomials [13], the unified Riccati equation method [14], the generalized Kudryashov method [15], and so on [16,17,18,19,20,21,22,23,24].
There is no unified method to obtain the exact solution of nonlinear partial differential equations. Although predecessors have obtained some analytical solutions with different methods, no scholar has studied the system with complete discrimination system for polynomial method.
The Fokas system is a very important class of nonlinear partial differential equations. In this article, we focus on the Fokas system, which is given as follows [25,26,27,28,29,30,31,32,33,34,35,36,37]
{ipt+r1pxx+r2pq=0,r3qy−r4(|p|2)x=0, | (1.1) |
where p=p(x,y,t) and q=q(x,y,t) are the complex functions which stand for the nonlinear pulse propagation in monomode optical fibers. The parameters r1,r2,r3 and r4 are arbitrary non-zero constants, which are coefficients of nonlinear terms in Eq (1.1) and reflect different states of optical solitons.
This paper is arranged as follows. In Section 2, we describe the method of the complete discrimination system for polynomial method. In Section 3, we substitute traveling wave transformation into nonlinear ordinary differential equations and obtain the different new single traveling wave solutions for the Fokas system by complete discrimination system for polynomial method. At the same time, we draw some images of solutions. In Section 4, the main results are summarized.
First, we consider the following partial differential equations:
{F(u,v,ux,ut,vx,vt,uxx,uxt,utt,⋯)=0, G(u,v,ux,ut,vx,vt,uxx,uxt,utt,⋯)=0, | (2.1) |
where F and G is polynomial function which is about the partial derivatives of each order of u(x,t) and v(x,t) with respect to x and t.
Step 1: Taking the traveling wave transformation u(x,t)=u(ξ),v(x,t)=v(ξ),ξ=kx+ct into Eq (2.1), then the partial differential equation is converted to an ordinary differential equation
{F(u,v,u′,v′,u″,v″,⋯)=0,G(u,v,u′,v′,u″,v″,⋯)=0. | (2.2) |
Step 2: The above nonlinear ordinary differential equations (2.2) are reduced to the following ordinary differential form after a series of transformations:
(u′)2=u3+d2u2+d1u+d0. | (2.3) |
The Eq (2.3) can also be written in integral form as:
±(ξ−ξ0)=∫du√u3+d2u2+d1u+d0. | (2.4) |
Step 3: Let ϕ(u)=u3+d2u2+d1u+d0. According to the complete discriminant system method of third-order polynomial
{Δ=−27(2d3227+d0−d1d23)2−4(d1−d223)3,D1=d1−d223, | (2.5) |
the classification of the solution of the equation can be obtained, and the classification of traveling wave solution of the Fokas system will be given in the following section.
In the current part, we obtain all exact solutions to Eq (1.1) by complete discrimination system for polynomial method. According to the wave transformation
p(x,y,t)=φ(η)ei(λ1x+λ2y+λ3t+λ4),q(x,y,t)=ϕ(η),η=x+y−vt, | (3.1) |
where λ1,λ2,λ3,λ4 and v are real parameters, and v represents the wave frame speed.
Substituting the above transformation Eq (3.1) into Eq (1.1), we get
{(−v+2r1λ1)iφ′−λ3φ+r1φ″−r1λ21φ+r2φϕ=0,r3ϕ′−2r4φφ′=0. | (3.2) |
Integrating the second equation in (3.2) and ignoring the integral constant, we get
ϕ(η)=r4φ2(η)r3. | (3.3) |
Substituting Eq (3.3) into the first equation in (3.2) and setting v=2r1λ1, we get the following:
r1φ″−(λ3+r1λ21)φ+r2r4φ3r3=0. | (3.4) |
Multiplying φ′ both sides of the Eq (3.4), then integrating once to get
(φ′)2=a4φ4+a2φ2+a0, | (3.5) |
where a4=−r2r42r1r3,a2=λ3+r1λ21r1, a0 is the arbitrary constant.
Let φ=±√(4a4)−13ω, b1=4a2(4a4)−23,b0=4a0(4a4)−13,η1=(4a4)13η. | (3.6) |
Equation (3.5) can be expressed as the following:
(ω′η1)2=ω3+b1ω2+b0ω. | (3.7) |
Then we can get the integral expression of Eq (3.7)
±(η1−η0)=∫dω√ω(ω2+b1ω+b0), | (3.8) |
where η0 is the constant of integration.
Here, we get the F(ω)=ω2+b1ω+b0 and Δ=b21−4b0. In order to solve Eq (3.7), we discuss the third order polynomial discrimination system in four cases.
Case 1:Δ=0 and ω>0.
When b1<0, the solution of Eq (3.7) is
ω1=−b12tanh2(12√−b12(η1−η0)). | (3.9) |
ω2=−b12coth2(12√−b12(η1−η0)). | (3.10) |
Thus, the classification of all solutions of Eq (3.7) is obtained by the third order polynomial discrimination system. The exact traveling wave solutions of the Eq (1.1) are obtained by combining the above solutions and the conditions (3.6) with Eq (3.1), can be expressed as below:
p1(x,y,t)=±√r3(λ3+r1λ21)r2r4tanh(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))⋅ei(λ1x+λ2y+λ3t+λ4). | (3.11) |
In Eq (3.11), p1(x,y,t) is a dark soliton solution, it expresses the energy depression on a certain intensity background. Figure 1 depict two-dimensional graph, three-dimensional graph, contour plot and density plot of the solution.
q1(x,y,t)=λ3+r1λ21r2tanh2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0)) | (3.12) |
p2(x,y,t)=±√r3(λ3+r1λ21)r2r4coth(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))⋅ei(λ1x+λ2y+λ3t+λ4), | (3.13) |
where p1(x,y,t),q1(x,y,t),p2(x,y,t),q2(x,y,t) are hyperbolic function solutions. Specially, p2(x,y,t) is a bright soliton solution.
q2(x,y,t)=λ3+r1λ21r2coth2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0)). | (3.14) |
When b1>0, the solution of Eq (3.7) is
ω3=b12tan2(12√b12(η1−η0)). | (3.15) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p3(x,y,t)=±√−r3(λ3+r1λ21)r2r4tan(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))⋅ei(λ1x+λ2y+λ3t+λ4). | (3.16) |
q3(x,y,t)=−λ3+r1λ21r2tan2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0)). | (3.17) |
In Eq (3.16) and Eq (3.17), p3(x,y,t) and q3(x,y,t) are trigonometric function solutions. q3(x,y,t) is a periodic wave solution, and it Shows the periodicity of q3(x,y,t) in Figure 2(a), (b).
When b1=0, the solution of Eq (3.7) is
ω4=4(η1−η0)2. | (3.18) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p4(x,y,t)=±√−(2r2r4r1r3)−132(2r2r4r1r3)13η+η0ei(λ1x+λ2y+λ3t+λ4), | (3.19) |
q4(x,y,t)=−r4r3(2r2r4r1r3)−134((2r2r4r1r3)13η+η0)2, | (3.20) |
where p4(x,y,t) is exponential function solution, and q4(x,y,t) is rational function solution.
Case 2: Δ=0 and b0=0.
When ω>−b1 and b1<0, the solution of Eq (3.7) is
ω5=b12tanh2(12√b12(η1−η0))−b1. | (3.21) |
ω6=b12coth2(12√b12(η1−η0))−b1. | (3.22) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p5(x,y,t)=±√−r3(λ3+r1λ21)r2r4(tanh2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))−2)⋅ei(λ1x+λ2y+λ3t+λ4), | (3.23) |
q5(x,y,t)=−λ3+r1λ21r2tanh2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, | (3.24) |
p6(x,y,t)=±√−r3(λ3+r1λ21)r2r4(coth2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))−2)⋅ei(λ1x+λ2y+λ3t+λ4), | (3.25) |
q6(x,y,t)=−λ3+r1λ21r2coth2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, | (3.26) |
where p5(x,y,t),q5(x,y,t),p6(x,y,t) and q6(x,y,t) are hyperbolic function solutions.
When ω>−b1 and b1>0, the solution of Eq (3.7) is
ω7=−b12tan2(12√−b12(η1−η0))−b1. | (3.27) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p7(x,y,t)=±√r3(λ3+r1λ21)r2r4(tan2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2)⋅ei(λ1x+λ2y+λ3t+λ4), | (3.28) |
q7(x,y,t)=λ3+r1λ21r2tan2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, | (3.29) |
where p7(x,y,t) and q7(x,y,t) are trigonometric function solutions.
Case 3: Δ>0 and b0≠0. Let u<v<s, there u,v and s are constants satisfying one of them is zero and two others are the root of F(ω)=0.
When u<ω<v, we can get the solution of Eq (3.7) is
ω8=u+(v−u)sn2(√s−u2(η1−η0),c), | (3.30) |
where c2=v−us−u.
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p8(x,y,t)=±√−(2r2r4r1r3)−13[u+(v−u)⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]⋅ei(λ1x+λ2y+λ3t+λ4). | (3.31) |
q8(x,y,t)=−r4r3(2r2r4r1r3)−13[u+(v−u)⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]. | (3.32) |
When ω>s, the solution of Eq (3.7) is
ω9=−v⋅sn2(√s−u(η1−η0)/2,c)+scn2(√s−u(η1−η0)/2,c). | (3.33) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p9(x,y,t)=±√−(2r2r4r1r3)−13−v⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]+scn2(√s−u2((2r2r4r1r3)13η+η0),c)ei(λ1x+λ2y+λ3t+λ4). | (3.34) |
q9(x,y,t)=−r4r3(2r2r4r1r3)−13−v⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]+scn2(√s−u2((2r2r4r1r3)13η+η0),c). | (3.35) |
Case 4: Δ<0.
When ω>0, similarly we get
ω10=2√b01+cn(b140(η1−η0),c)−√b0, | (3.36) |
where c2=(1−b1√b02)/2.
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p10(x,y,t)=±√2√a0(2r2r4r1r3)−12[−21+cn((4a0(−2r2r4r1r3)−13)14((2r2r4r1r3)13η+η0),c)+1]⋅ei(λ1x+λ2y+λ3t+λ4), | (3.37) |
q10(x,y,t)=−r4r32√a0(2r2r4r1r3)−12[−21+cn((4a0(−2r2r4r1r3)−13)14((2r2r4r1r3)13η+η0),c)+1], | (3.38) |
where p8(x,y,t),q8(x,y,t),p9(x,y,t),q9(x,y,t),p10(x,y,t) and q10(x,y,t) are Jacobian elliptic function solutions.
In this paper, the complete discrimination system of polynomial method has been applied to construct the single traveling wave solutions of the Fokas system. The Jacobian elliptic function solutions, the trigonometric function solutions, the hyperbolic function solutions and the rational function solutions are obtained. The obtained solutions are very rich, which can help physicists understand the propagation of traveling wave in monomode optical fibers. Furthermore, we have also depicted two-dimensional graphs, three-dimensional graphs, contour plots and density plots of the solutions of Fokas system, which explains the state of solitons from different angles.
This work was supported by Scientific Research Funds of Chengdu University (Grant No.2081920034).
The authors declare no conflict of interest.
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