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Research article

Traveling wave solution of (3+1)-dimensional negative-order KdV-Calogero-Bogoyavlenskii-Schiff equation

  • We explored the (3+1)-dimensional negative-order Korteweg-de Vries-alogero-Bogoyavlenskii-Schiff (KdV-CBS) equation, which develops the classical Korteweg-de Vries (KdV) equation and extends the contents of nonlinear partial differential equations. A traveling wave transformation is employed to transform the partial differential equation into a system of ordinary differential equations linked with a cubic polynomial. Utilizing the complete discriminant system for polynomial method, the roots of the cubic polynomial were classified. Through this approach, a series of exact solutions for the KdV-CBS equation were derived, encompassing rational function solutions, Jacobi elliptic function solutions, hyperbolic function solutions, and trigonometric function solutions. These solutions not only simplified and expedited the process of solving the equation but also provide concrete and insightful expressions for phenomena such as optical solitons. Presenting these obtained solutions through 3D, 2D, and contour plots offers researchers a deeper understanding of the properties of the model and allows them to better grasp the physical characteristics associated with the studied model. This research not only provides a new perspective for the in-depth exploration of theoretical aspects but also offers valuable guidance for the practical application and advancement of related technologies.

    Citation: Musong Gu, Chen Peng, Zhao Li. Traveling wave solution of (3+1)-dimensional negative-order KdV-Calogero-Bogoyavlenskii-Schiff equation[J]. AIMS Mathematics, 2024, 9(3): 6699-6708. doi: 10.3934/math.2024326

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  • We explored the (3+1)-dimensional negative-order Korteweg-de Vries-alogero-Bogoyavlenskii-Schiff (KdV-CBS) equation, which develops the classical Korteweg-de Vries (KdV) equation and extends the contents of nonlinear partial differential equations. A traveling wave transformation is employed to transform the partial differential equation into a system of ordinary differential equations linked with a cubic polynomial. Utilizing the complete discriminant system for polynomial method, the roots of the cubic polynomial were classified. Through this approach, a series of exact solutions for the KdV-CBS equation were derived, encompassing rational function solutions, Jacobi elliptic function solutions, hyperbolic function solutions, and trigonometric function solutions. These solutions not only simplified and expedited the process of solving the equation but also provide concrete and insightful expressions for phenomena such as optical solitons. Presenting these obtained solutions through 3D, 2D, and contour plots offers researchers a deeper understanding of the properties of the model and allows them to better grasp the physical characteristics associated with the studied model. This research not only provides a new perspective for the in-depth exploration of theoretical aspects but also offers valuable guidance for the practical application and advancement of related technologies.



    Abbreviations: VS: Vector space; HVS: Hyper vector space; SHS: Sub-hyperspace; LT: Linear transformation; FS: Fuzzy set; IFS: Intuitionistic fuzzy set; PFS: Picture fuzzy set; PFSs: Picture fuzzy sets; MMS: Measure of membership; MNonMS: Measure of non-membership; MPMS: Measure of positive membership; MNeuMS: Measure of neutral membership; MNegMS: Measure of negative membership; MRefMS: Measure of refusal membership; CP: Cartesian product; FSHS: Fuzzy sub-hyperspace; FSS: Fuzzy subspace; PFSHS: Picture fuzzy sub-hyperspace; PFSS: Picture fuzzy subspace; PFLT: Picture fuzzy linear transformation; PFLTs: Picture fuzzy linear transformations

    Uncertainty is a part of our real life. In almost each and every problem, uncertainty arises. Classical set theory is only capable to handle certain cases. Uncertain case can not be handled with classical point view. As an uncertainty handling tool, fuzzy set was initiated by Zadeh [1]. It is necessary to mention that fuzzy set deals only with the grade of membership. It is in fact the extension of classical sense. Certain case can be easily derived from uncertain cases by some particular choice of uncertainty measurement parameters. That is why fuzzy set theory is so more popular than classical set theory. After the introduction of fuzzy set, many researchers worked on fuzzy set based on algebraic structures. Subspace in fuzzy environment was introduced by Katsaras and Liu [2]. Fuzzy vector space under triangular norm was investigated by Das [3]. Idea of Das was enriched by Kumar [4]. Fuzzy set theory was used to solve different types of linear programming problems, transportation problems by the researchers [5,6,7,8]. First study on hyperstructure was done by Marty [9] in the form hypergroup. After that several researchers [10,11,12] worked on different types of hyperstructures. Fuzzy hypergroup and fuzzy hypermodule was studied by Davvaz [13,14]. Hyper vector space, an important kind of algebraic hyperstructure, was initiated by Tallini [15]. Ameri and Dehghan [16] applied fuzzy set in hyper vector space. In case of clear information, grade of non-membership =1 grade of membership. But for doubtful information, this rule does not work. In such cases, individual measurements of grade of membership and non-membership become necessary. Implementation of this concept was done in the form of intuitionistic fuzzy set by Atanassov [17]. In the field of medical sciences, sciences and social sciences; it is observed that two components are not sufficient to represent some special type of information. For instance, a voter can give vote in favour of a candidate (positive sense), against a candidate (negative sense) or he/she may remain neutral. Keeping this type of situation in mind, generalization on intuitionistic fuzzy set was done by Cuong [18] in the form of picture fuzzy set (dealing with grade of positive membership, grade of neutral membership and grade of negative membership). With the advancement of time, different kinds of research works in picture fuzzy environment were done by several researchers [19,20,21,22,23,24]. A lot of research works on picture fuzzy set based on algebraic structures were done by Dogra and Pal [25,26,27,28,29].

    Algebraic hyperstructures are the generalizations of algebraic structures. It is well known that algebraic hyperoperation of two elements gives a set whereas algebraic operation of two elements gives a single element. When algebraic hyperoperation of two elements gives a singleton set, then algebraic hyperstructure is reduced to algebraic structure. Algebraic hyperstructure is an important field of study not only in Mathematics, but also in Computer Science. In real life, uncertainty arises in different forms i.e. types of uncertainty are not always same. When fuzzy and intuitionistic fuzzy tools are not capable to handle uncertainty with one or two uncertainty measurement parameters, then more uncertainty measurement parameters are required to introduce. In this purpose, picture fuzzy set was initiated by Cuong [18]. Algebraic hyperstructures become complicated when the number of uncertainty measurement parameters increases. Study of algebraic hyperstructures under such type of complicated environment is a challenging task to the researchers. If this challenge can be handled, then the study of algebraic hyperstructures in less complicated atmosphere will be quite easy as its consequence. Algebraic hyperstructure is an abstract idea and to show the application of this abstract concept in decision making problem under fuzzy/advanced fuzzy environment is a difficult task as this type of application is not available till now in existing literature. If this type of application can be developed then it will add a new dimension not only in the field of algebra but also in the field of decision making. This allows enough motivation to study the properties of algebraic hyperstructures and their applications under advanced fuzzy environment.

    In this paper, we introduce the notion of picture fuzzy sub-hyperspaces of a hyper vector space and investigate some results related to these on the basis of some elementary operations (intersection, union, Cartesian product etc.) on picture fuzzy sets. We initiate the concept of picture fuzzy linear transformation and study some important results in this regard. We show that linear combination of two picture fuzzy linear transformations is a picture fuzzy linear transformation, composition of two picture fuzzy linear transformations is a picture fuzzy linear transformation and inverse of bijective picture fuzzy linear transformation is a picture fuzzy linear transformation. We discuss the effect of good linear transformation on picture fuzzy sub-hyperspaces. We show that the image of a picture fuzzy sub-hyperspace is a picture fuzzy sub-hyperspace under bijective good linear transformation and the inverse image of a picture fuzzy sub-hyperspace is a picture fuzzy sub-hyperspace under good linear transformation. We study some important results on picture fuzzy sub-hyperspaces in the light of (θ,ϕ,ψ)-cut of picture fuzzy sets. Finally, we present an application of picture fuzzy sub-hyperspace conditions in an interesting decision making problem. This decision making is presented here from algebraic point of view.

    In this section, we recapitulate the concepts of FS, IFS, hyperoperation, HVS, LT in HVS, SHS, FSHS, PFS, CP of PFSs, (θ,ϕ,ψ)-cut of PFS, image of PFS, inverse image of PFS.

    As an extension of classical set theoretic concept, FS was invented by Zadeh [1].

    Definition 2.1. [1] Let ξ be a set of universe. Then a FS over ξ is defined as τ={(a,τ1(a)):aξ}, where τ1(a)[0,1] is the MMS of a in ξ.

    The concepts of hyperoperation and HVS are defined as follows.

    Definition 2.2. [15] A mapping :M×MP(M) is called a hyperoperation, where P(M) is the power set of the set M excluding the null set. This operation can be extended in case of subsets of M as follows.

    AB={ab:aA,bB}.

    aA={a}A.

    and Aa=A{a}.

    Definition 2.3. [15] Let (ξ,+) be an abelian group and F be a field. Then (ξ,+,) forms a HVS over F if

    (i) p(a+b)p+pb

    (ii) (p+q)apa+qa

    (iii) p(qa)=(pq)a

    (iv) p(a)=(p)a=(pa)

    (v) a1a for all a,bξ and for all p,qF.

    LT in HVS is defined as follows.

    Definition 2.4. [15] Let ξ1 and ξ2 be two HVSs over the same field F. Then a map T:ξ1ξ2 is said to be LT on ξ1 if

    (i) T(a+b)=T(a)+T(b)

    (ii) T(pa)pT(a) for all a,bξ and for all pF.

    Note that T is called good LT if

    (i) T(a+b)=T(a)+T(b)

    (ii) T(pa)=pT(a) for all a,bξ and for all pF.

    SHS of a HVS is defined as follows.

    Definition 2.5. [15] Let ξ be a hyper vector space over a field F and S be a subset of ξ. Then S is called a SHS of ξ if pF and aSpaS.

    The concept of SHS in fuzzy setting is as follows.

    Definition 2.6. [16] Let ξ be a hyper vector space over a field F. A FS τ={(a,τ1(a)):aξ} over ξ is said to be FSHS of ξ if

    (i) τ1(ab)τ1(a)τ1(b)

    (ii) infcpaτ1(c)τ1(a) for all aξ and for all pF.

    Eliminating the limitation of FS, Atanassov [17] defined IFS as an extended version of FS.

    Definition 2.7. [17] An IFS τ over a set of universe ξ is defined as τ={(a,τ1(a),τ2(a),):aξ}, where τ1(a)[0,1] is the MMS and τ2(a)[0,1] is the MNonMS of a satisfying the condition 0τ1(a)+τ2(a)+τ3(a)1 for all aξ.

    Including more possible types of uncertainty, Cuong [18] defined PFS generalizing the concepts of FS and IFS.

    Definition 2.8. [18] A PFS τ over a set of universe ξ is defined as τ={(a,τ1(a),τ2(a),τ3(a)):aξ}, where τ1(a)[0,1] is the MPMS of a in ξ, τ2(a)[0,1] is the MNeuMS of a in τ and τ3(a)[0,1] is the MNegMS of a in τ with the condition 0τ1(a)+τ2(a)+τ3(a)1 for all aξ. For all aξ, 1(τ1(a)+τ2(a)+τ3(a)) is the MRefMS a in τ.

    Some basic operations on PFSs are as follows.

    Definition 2.9. [18] Let τ={(a,τ1(a),τ2(a),τ3(a)):aξ} and τ={(a,τ1(a),τ2(a),τ3(a)):aξ} be two PFSs over a set of universe ξ. Then

    (i) ττ iff τ1(a)τ1(a), τ2(a)τ2(a), τ3(a)τ3(a) for all aξ.

    (ii) τ=τ iff τ1(a)=τ1(a),τ2(a)=τ2(a),τ3(a)=τ3(a) for all aξ.

    (iii) ττ={(a,max(τ1(a),τ1(a)),min(τ2(a),τ2(a)),min(τ3(a),τ3(a))):aξ}.

    (iv) ττ={(a,min(τ1(a),τ1(a)),min(τ2(a),τ2(a)),max(τ3(a),τ3(a))):aξ}.

    The CP of two PFSs is defined below.

    Definition 2.10. [18] Let τ={(a,τ1(a),τ2(a),τ3(a)):aξ1} and τ={(b,τ1(b),τ2(b),τ3(b)):bξ2} be two PFSs over two sets of universe A1 and A2 respectively. Then the CP of τ and τ is the PFS τ×τ = {((a,b),ξ1((a,b)),ξ2((a,b)),ξ3((a,b))):(a,b)ξ1×ξ2}, where ξ1((a,b))=τ1(a)τ1(b), ξ2((a,b))=τ2(a)τ2(b) and ξ3((a,b))=τ3(a)τ3(b) for all (a,b)ξ1×ξ2.

    The (θ,ϕ,ψ)-cut of a PFS is defined as follows.

    Definition 2.11. [18] Let τ={(a,τ1(a),τ2(a),τ3(a)):aξ} be a PFS over a set of universe ξ. Then (θ,ϕ,ψ)-cut of τ is the crisp set in ξ denoted by Cθ,ϕ,ψ(τ) and is defined as Cθ,ϕ,ψ(τ)={aξ:τ1(a)θ,τ2(a)ϕ,τ3(a)ψ}, where θ,ϕ,ψ[0,1] with the condition 0θ+ϕ+ψ1.

    The image of a PFS is defined as follows.

    Definition 2.12. [18] Let ξ1 and ξ2 be two sets of universe. Let h:ξ1ξ2 be a surjective mapping and τ={(a1,τ1(a1),τ2(a1),τ3(a1)):a1ξ1} be a PFS over ξ1. Then the image of τ under the map h is the PFS h(τ)={(a2,ψ1(a2),ψ2(a2),ψ3(a2)):a2ξ2}, where ψ1(a2)=a1h1(a2)τ1(a1), ψ2(a2)=a1h1(a2)τ2(a1) and ψ3(a2)=a1h1(a2)τ3(a1) for all a2ξ2.

    The inverse image of a PFS is defined as follows.

    Definition 2.13. [18] Let ξ1 and ξ2 be two sets of universe. Let h:ξ1ξ2 be a mapping and τ={(a2,τ1(a2),τ2(a2),τ3(a2)):a2ξ2} be a PFS over ξ2. Then the inverse image of τ under the map h is the PFS h1(τ)={(a1,ψ1(a1),ψ2(a1),ψ3(a1)):a1ξ1}, where ψ1(a1)=τ1(h(a1)), ψ2(a1)=τ2(h(a1)) and ψ3(a1)=τ3(h(a1)) for all a1ξ1.

    Throughout the paper, we write PFS τ={(a,τ1(a),τ2(a),τ3(a)):aξ} as τ=(τ1,τ2,τ3).

    In this section, the notion of PFSHS of a HVS is introduced as a generalization of FSHS of a HVS and some important results are investigated in this regard. Some properties of PFSHSs in the light of (θ,ϕ,ψ)-cut of PFS are studied here. An application of PFSHS conditionin decision making problem is shown here.

    First we are going to define PFSHS as a generalization of FSHS.

    Definition 3.1. Let ξ be a HVS over a field F. A PFS τ=(τ1,τ2,τ3) over the set of universe ξ is said to be PFSHS of ξ if

    (i) τ1(ab)τ1(a)τ1(b), τ2(ab)τ2(a)τ2(b) and τ3(ab)τ3(a)τ3(b)

    (ii) infcpaτ1(c)τ1(a), infcpaτ2(c)τ2(a) and supcpaτ3(c)τ3(a) for all aξ and for all pF.

    Now, it is the time to establish some elementary results on PFSHS. The following proposition gives a relation between the null vector and any other vector in a HVS over which PFSHS is defined. This relationship is given here in terms of picture fuzzy membership values.

    Proposition 3.1. Let ξ be a HVS over a field F and τ=(τ1,τ2,τ3) be a PFSHS of ξ. Then

    (i) τ1(ρ)τ1(a), τ2(ρ)τ2(a) and τ3(ρ)τ3(a)

    (ii) infc1aτ1(c)=τ1(a),infc1aτ2(c)=τ2(a) and supc1aτ3(c)=τ3(a) for all a,bξ.

    Proof: (i) Since τ is a PFSHS of ξ therefore τ1(ρ)=τ1(aa)τ1(a), τ2(ρ)=τ2(aa)τ2(a)τ2(a)=τ2(a) and τ3(ρ)=τ3(aa)τ3(a)τ3(a)=τ3(a) for all aξ.

    (ii) Since τ is a PFSHS therefore infc1aτ1(c)τ1(a), infc1aτ2(c)τ2(a) and supc1aτ3(c)τ3(a) for all aξ.

    Since a1a therefore τ1(a)infc1aτ1(c), τ2(a)infc1aτ2(c) and τ3(a)supc1aτ3(c) for all aξ.

    Thus, infc1aτ1(c)=τ1(a),infc1aτ2(c)=τ2(a) and supc1aτ3(c)=τ3(a) for all aξ.

    It is observed that for p,qF and a,bξ, pa+qbP(ξ) (P(ξ)=P(ξ)ϕ). If τ=(τ1,τ2,τ3) be a PFS over pa+qb then we define infc=c1+c2pa+qbτ1(c)=infc1paτ1(c1)infc2paτ1(c2), infc=c1+c2pa+qbτ2(c)=infc1paτ2(c1)infc2paτ2(c2) and supc=c1+c2pa+qbτ3(c)=supc1paτ3(c1)supc2paτ3(c2). This definition will be useful to establish crucial results on PFSHSs.

    Now, we are going to propose a necessary and sufficient condition under which a PFS will be a PFSHS.

    Proposition 3.2. Let ξ be a HVS over a field F and τ=(τ1,τ2,τ3) be a PFS over ξ. Then τ is a PFSHS of ξ iff infcpa+qbτ1(c)τ1(a)τ1(b), infcpa+qbτ2(c)τ2(a)τ2(b) and supcpa+qbτ3(c)τ3(a)τ3(b) for all a,bξ and for all p,qF.

    Proof: Let τ be a PFSHS of ξ. Therefore,

    infc=c1+c2pa+qbτ1(c)=[infc1paτ1(c1)][infc2paτ1(c2)]τ1(a)τ1(b),

    infc=c1+c2pa+qbτ2(c)=[infc1paτ2(c1)][infc2paτ2(c2)]τ2(a)τ2(b)

    and supc=c1+c2pa+qbτ3(c)=[supc1paτ3(c1)][supc2paτ3(c3)]τ3(a)τ3(b) for all a,bξ and for all p,qF.

    Conversely, let the conditions infcpa+qbτ1(c)τ1(a)τ1(b), infcpa+qbτ2(c)τ2(a)τ2(b) and supcpa+qbτ3(c)τ3(a)τ3(b) for all a,bξ and for all p,qF be hold. Then putting p = 1, q=1 it is obtained that infc1a+(1)bτ1(c)τ1(a)τ1(b), infc1a+(1)bτ2(c)τ2(a)τ2(b) and supc1a+(1)bτ3(c)τ3(a)τ3(b), that is, infcabτ1(c)τ1(a)τ1(b), infcabτ2(c)τ2(a)τ2(b) and supcabτ3(c)τ3(a)τ3(b) for all a,bξ. Now, putting q=ρ in the given conditions it is obtained that infcpa+qρτ1(c)τ1(a)τ1(ρ)=τ1(a), infcpa+qρτ2(c)τ2(a)τ2(ρ)=τ2(a) and supcpa+qρτ3(c)τ3(a)τ3(ρ)=τ3(a), that is, infcpaτ1(c)τ1(a), infcpaτ2(c)τ2(a) and supcpaτ3(c)τ3(a) for all aξ and for all pF. Hence, τ is a PFSHS of ξ.

    Proposition 3.3. Let ξ be a HVS over a field F and τ=(τ1,τ2,τ3) be a PFSHS of ξ. Then Cθ,ϕ,ψ(τ) is a SHS of ξ.

    Proof: Let a,bCθ,ϕ,ψ(τ). Then τ1(a)θ, τ2(a)ϕ, τ3(a)ψ and τ1(b)θ, τ2(b)ϕ, τ3(b)ψ.

    Now, τ1(ab)τ1(a)τ1(b)θθ=θ, τ2(ab)τ2(a)τ2(b)ϕϕ=ϕ and τ3(ab)τ3(a)τ3(b)ψψ=ψ.

    Also, infcpaτ1(c)τ1(a)θ, infcpaτ2(c)τ2(a)ϕ and supcpaτ3(c)τ3(a)ϕ. Hence, τ1(c)θ, τ2(c)ϕ and τ3(c)ψ for all cpa. Thus, pF and aCθ,ϕ,ψ(τ)paCθ,ϕ,ψ(τ). This implies Cθ,ϕ,ψ(τ) is a SHS of ξ.

    Proposition 3.4. Let ξ be a HVS over a field F and τ=(τ1,τ2,τ3) be a PFS over ξ. Then τ is a PFSHS of ξ if all (θ,ϕ,ψ)-cuts of τ are SHSs of ξ.

    Proof: Let a,bξ. Also, let θ=τ1(a)τ1(b), ϕ=τ2(a)τ2(b) and ψ=τ3(a)τ3(b). Then θ[0,1],ϕ[0,1] and ψ[0,1] with 0θ+ϕ+ψ1.

    Now, τ1(a)τ1(a)τ2(b)=θ, τ2(a)τ2(a)τ2(b)=ϕ, τ3(a)τ3(a)τ3(b)=ψ and τ1(b)τ1(a)τ1(b)=θ, τ2(b)τ2(a)τ2(b)=ϕ, τ3(b)τ3(a)τ3(b)=ψ. Therefore, a,bCθ,ϕ,ψ(τ). Since Cθ,ϕ,ψ(τ) is a SHS of ξ therefore abCθ,ϕ,ψ(τ). This implies, τ1(ab)θ=τ1(a)τ1(b), τ2(ab)ϕ=τ2(a)τ2(b) and τ3(ab)ψ=τ3(a)τ3(b). Since a and b are arbitrary elements of ξ therefore τ1(ab)τ1(a)τ1(b), τ2(ab)τ2(a)τ2(b) and τ3(ab)τ3(a)τ3(b) for all a,bξ.

    Now, let τ1(a)=θ1, τ2(a)=ϕ1 and τ3(a)=ψ1. Then θ1[0,1], ϕ1[0,1] and ψ1[0,1] with 0θ1+ϕ1+ψ11. Since Cθ1,ϕ1,ψ1(τ) is a SHS of ξ therefore pF and aCθ,ϕ,ψ(τ) implies paCθ1,ϕ1,ψ1(τ). So, for any cpa, infcpaτ1(c)θ1=τ1(a), infcpaτ2(c)ϕ1=τ2(a) and supcpaτ3(c)ψ1=τ3(a). Thus, τ is a PFSHS of ξ.

    Proposition 3.5. Let ξ be a HVS over a field F and τ=(τ1,τ2,τ3), τ=(τ1,τ2,τ3) be two PFSHSs of ξ. Then ττ is a PFSHS of ξ.

    Thus, it is observed that the intersection of two PFSHSs is a PFSHS. But, the union of two PFSHSs is not necessarily a PFSHS which can be shown by the following example.

    Example 3.1. Let ξ=R2. Then (ξ,+,) forms a HVS under vector addition + and external composition defined by

    pa={A,whena(0,0){(0,0)},whena=(0,0)

    where A the collection of the points on the line joining the point a and (0, 0).

    Now, take S1={(b,0):bR} and S2={(0,c):cR}. Then (S1,+,) and (S2,+,) are SHSs of ξ where is defined by

    p(b,0)={S1,whenb0{(0,0)},otherwise
    p(0,c)={S2,whenc0{(0,0)},otherwise

    Now, let us consider two PFSs τ=(τ1,τ2,τ3) and τ=(τ1,τ2,τ3) over ξ as follows.

    τ1(a)={0.45,whenaS10.2,otherwise
    τ2(a)={0.4,whenaS10.25,otherwise

    and

    τ3(a)={0.15,whenaS10.48,otherwise
    τ1(a)={0.35,whenaS20.25,otherwise
    τ2(a)={0.3,whenaS20.2,otherwise

    and

    τ3(a)={0.27,whenaS20.39,otherwise

    Thus, ττ=ψ=(ψ1,ψ2,ψ3) is given by

    ψ1(a)={0.45,whenaS10.35,whenaS2{0}0.25,whenaξS1S2
    ψ2(a)={0.2,whenaS10.25,whenaS2{0}0.2,whenaξS1S2
    ψ3(a)={0.15,whenaS10.27,whenaS2{0}0.39,whenaξS1S2

    Here, C0.35,0.2,0.27(ττ)={a:τ1(a)0.35,τ2(a)0.2,τ3(a)0.27}={aξ:τ1(a)0.35}{aξ:τ2(a)0.2}{aξ:τ3(a)0.27}={aξ:τ1(a)=0.35,0.45}{aξ:τ2(a)=0.2,0.25}{aξ:τ3(a)=0.15,0.27}=(S1S2)ξ(S1S2)=S1S2.

    Since S1S2 is not a SHS of ξ therefore ψ=ττ is not a PFSHS of ξ, although τ and τ are PFSHSs of ξ.

    Proposition 3.6. Let ξ be a HVS over a field F and τ=(τ1,τ2,τ3), τ=(τ1,τ2,τ3) be two PFSHSs of ξ. Then ττ is a PFSHS of ξ if either ττ or ττ.

    From the above proposition, it is observed that the union of two PFSHSs is a PFSHS if one is subset of another. This condition is a sufficient condition for union of two PFSHSs to be a PFSHS. But the condition is not necessary which can be shown by the following example.

    Example 3.2. Let us consider the HVS given in Example 3.1. Now, let us consider PFSHs τ=(τ1,τ2,τ3) and τ=(τ1,τ2,τ3) over ξ as follows.

    τ1(a)={0.45,whena=(0,0)0.2,otherwise
    τ2(a)={0.4,whena=(0,0)0.25,otherwise

    and

    τ3(a)={0.15,whena=(0,0)0.48,otherwise
    τ1(a)={0.35,whena=(0,0)0.25,otherwise
    τ2(a)={0.3,whena=(0,0)0.2,otherwise

    and

    τ3(a)={0.27,whena=(0,0)0.39,otherwise

    Thus, ττ=ψ=(ψ1,ψ2,ψ3) is given by

    ψ1(a)={0.45,whena=(0,0)0.25,otherwise
    ψ2(a)={0.3,whena=(0,0)0.2,otherwise
    ψ3(a)={0.15,whena=(0,0)0.39,otherwise

    Notice that C0.27,0.25,0.37(ττ)={aξ:τ1(a)0.27}{aξ:τ2(a)0.25}{aξ:τ3(a)0.37}={aξ:τ1(a)=0.45}{aξ:τ2(a)=0.3}{aξ:τ3(a)=0.15}={(0,0)}

    and C0.25,0.2,0.39(ττ)={aξ:τ1(a)0.25}{aξ:τ2(a)0.2}{aξ:τ3(a)0.39}={aξ:τ1(a)=0.25,0.45}{aξ:τ2(a)=0.2,0.3}{aξ:τ3(a)=0.15,0.39}=ξ=R2.

    Here C0.27,0.25,0.37(ττ) and C0.25,0.2,0.39(ττ) are SHSs of ξ although neither ττ nor ττ.

    In this section, the notion of PFLT is initiated in a very interesting way which is different from existing literature. Also, some properties of PFLT are investigated.

    Definition 4.1. Let ξ1, ξ2 be two HVSs over the same field F and τ=(τ1,τ2,τ3),τ=(τ1,τ2,τ3) be two PFSHSs of ξ1 and ξ2 respectively. Also, let T:ξ1ξ2 be a mapping. Then T is said to be PFLT on ξ1 if

    (i) T is a linear transformation in crisp sense.

    (ii) τ1(T(a))τ1(a), τ2(T(a))τ2(a) and τ3(T(a))τ3(a) for all aξ1.

    Example 4.1. Let us consider the Example 3.1. Let us define a map T:ξξ by T((a1,a2))=(a1+a2,0) for all (a1,a2)ξ. Clearly, T is a linear map in crisp sense. For any (a1,a2)ξ, it is observed that

    τ1(T(a1,a2))=τ1((a1+a2,0))=0.45τ1((a1,a2)),τ2(T(a1,a2))=τ2((a1+a2,0))=0.4τ2((a1,a2))andτ3(T(a1,a2))=τ3((a1+a2,0)=0.15τ3((a1,a2)).

    Thus, T is a PFLT on ξ with respect to PFSHS τ.

    The following proposition states that linear combination of two PFLTs is a PFLT.

    Proposition 4.1. Let ξ1,ξ2 be two HVSs over the same field F and τ=(τ1,τ2,τ3),τ=(τ1,τ2,τ3) be two PFSHSs of ξ1,ξ2 respectively. If T1 and T2 be two PFLTs on ξ1 then so is aT1+bT2 for some a,bF.

    Proof:We have,

    infc(aT1+bT2)(d)τ1(c)=infc=c1+c2aT1(d)+bT2(d)τ1(c)=infc1aT1(d)τ1(c1)infc2bT2(d)τ1(c2)τ1(T1(d))τ1(T2(d))[asτisaPFSHSofξ2]τ1(d)τ1(d)[asT1,T2arePFLTsonξ1]=τ1(d)
    infc(aT1+bT2)(d)τ2(c)=infc=c1+c2aT1(d)+bT2(d)τ2(c)=infc1aT1(d)τ2(c1)infc2bT2(d)τ2(c2)τ2(T1(d))τ2(T2(d))[asτisaPFSHSofξ2]τ2(d)τ2(d)[asT1,T2arePFLTsonξ1]=τ2(d)
    andsupc(aT1+bT2)(d)τ3(c)=supc=c1+c2aT1(d)+bT2(d)τ3(c)=supc1aT1(d)τ3(c1)supc2bT2(d)τ3(c2)τ3(T1(d))τ3(T2(d))[asτisaPFSHSofξ2]τ3(d)τ3(d)[asT1,T2arePFLTsonξ1]=τ3(d)

    Thus, aT1+bT2 is a PFLT on ξ1 for some scalar a,bF.

    The following proposition states that composition of two PFLTs is a PFLT.

    Proposition 4.2. Let ξ1,ξ2,ξ3 be three HVSs over the same field F and τ=(τ1,τ2,τ3),τ=(τ1,τ2,τ3),τ=(τ1,τ2,τ3) be three PFSHSs of ξ1, ξ2, ξ3 respectively. If T1:ξ1ξ2 and T2:ξ2ξ3 be two PFLTs then so is T2T1.

    Proof:Let aξ1.

    Now,τ1((T2T1)(a))=τ1(T2(T1(a))τ1(T1(a))[becauseT2isaPFLT]τ1(a)[becauseT1isaPFLT]
    τ2((T2T1)(a))=τ2(T2(T1(a))τ2(T1(a))[becauseT2isaPFLT]τ2(a)[becauseT1isaPFLT]
    τ3((T2T1)(a))=τ3(T2(T1(a))τ3(T1(a))[becauseT2isaPFLT]τ3(a)[becauseT1isaPFLT]

    Since a is an arbitrary element of ξ1 therefore τ1((T2T1)(a))τ1(a) and τ2((T2T1)(a))τ2(a) and τ3((T2T1)(a))τ3(a) for all aξ1. Consequently, T2T1 is a PFLT on ξ1.

    The following proposition states that the inverse of a PFLT is a PFLT when the PFLT is bijective.

    Proposition 4.3. Let ξ1 and ξ2 be two HVSs over the same field F and τ=(τ1,τ2,τ3) be a PFSHS of ξ1. If T:ξ1ξ2 is a bijective good PFLT then T1 is a PFLT on ξ2.

    Proof:Let T(τ)=ψ=(ψ1,ψ2,ψ3). For bξ2, we have,

    ψ1(b)=aT1(b)τ1(a)ψ2(b)=aT1(b)τ2(a)andψ3(b)=aT1(b)τ3(a).

    Since T is bijective therefore T1(b) must be a singleton set. So, for bξ2, there exists an unique aξ1 such that a=T1(b) i.e. T(a)=b. Thus, in this case, ψ1(b)=τ1(a) and ψ2(b)=τ2(a) and ψ3(b)=τ3(a), that is, ψ1(b)=τ1(T1(b)), ψ2(b)=τ2(T1(b)) and ψ3(b)=τ3(T1(b)).

    Thus, it can be written as τ1(T1(b))=ψ1(b)ψ1(b), τ2(T1(b))=ψ2(b)ψ2(b) and τ3(T1(b))=ψ3(b)ψ3(b) for all bξ2. Hence, T1 is a PFLT on ξ2.

    In this section, we establish two propositions to discuss the effect of LT on PFSHSs. The first proposition states that the image of a PFSHS under bijective good LT is a PFSHS and the second proposition states that the inverse image of a PFSHS is a PFSHS under good LT.

    Proposition 5.1. Let ξ1 and ξ2 be two HVSs over the same field F and τ=(τ1,τ2,τ3) be a PFSHS of ξ1. Then for a bijective good LT T:ξ1ξ2, T(τ) is a PFSHS of ξ2.

    Proof: Let T(τ)=ψ=(ψ1,ψ2,ψ3). For cξ2, we have,

    ψ1(c)=aT1(c)τ1(a)ψ2(c)=aT1(c)τ2(a)andψ3(c)=aT1(b)τ3(a).

    Since T is bijective therefore T1(c) must be a singleton set. So, for cξ2, there exists an unique aξ1 such that a=T1(c) i.e. T(a)=c. Thus, in this case, ψ1(c)=ψ1(T(a))=τ1(a), ψ2(c)=ψ2(T(a))=τ2(a) and ψ3(c)=ψ3(T(a))=τ3(a). Similarly, for dξ2, ψ1(d)=ψ1(T(b))=τ1(b), ψ2(d)=ψ2(T(b))=τ2(b) and ψ3(d)=ψ3(T(b))=τ3(b).

    Now,infzpc+qdψ1(z)=infzpT(a)+qT(b)ψ1(z)[wherec=T(a)andd=T(b)]=infzT(pa+qb)ψ1(z)[asTisagoodLT]=infs=s1+s2pa+qbτ1(s)[asTisabijectiveLTandz=T(s)foruniquesξ1]=infs1paτ1(s1)infs2qbτ1(s2)τ1(a)τ1(b)=ψ1(T(a))ψ1(T(b))=ψ1(c)ψ1(d)
    infzpc+qdψ2(z)=infzpT(a)+qT(b)ψ2(z)[wherec=T(a)andd=T(b)]=infzT(pa+qb)ψ2(z)[asTisagoodLT]=infs=s1+s2pa+qbτ2(s)[asTisabijectiveLTandz=T(s)foruniquesξ1]=infs1paτ2(s1)infs2qbτ2(s2)τ2(a)τ2(b)=ψ2(T(a))ψ2(T(b))=ψ2(c)ψ2(d)
    supzpc+qdψ3(z)=supzpT(a)+qT(b)ψ3(z)[wherec=T(a)andd=T(b)]=supzT(pa+qb)ψ3(z)[asTisagoodLT]=sups=s1+s2pa+qbτ3(s)[asTisabijectiveLTandz=T(s)foruniquesξ1]=sups1paτ3(s1)sups2qbτ3(s2)τ3(a)τ3(b)=ψ3(T(a))ψ3(T(b))=ψ3(c)ψ3(d)

    Since, c,d are arbitrary elements of ξ2 therefore infzpc+qdψ1(z)ψ1(c)ψ1(d), infzpc+qdψ2(z)ψ2(c)ψ2(d) and supzpc+qdψ3(z)ψ3(c)ψ3(d) for all c,dξ2 and for all p,qF. Consequently, T(τ) is a PFSHS of ξ2.

    Proposition 5.2. Let ξ1 and ξ2 be two HVSs over the same field F and τ=(τ1,τ2,τ3) be a PFSHS of ξ2. Also, let T:ξ1ξ2 be a good LT. Then T1(τ) is a PFSHS of ξ1.

    Proof: Let T1(τ) = ψ = (ψ1,ψ2,ψ3), where ψ1(a)=τ1(T(a)) and ψ2(a)=τ2(T(a)) and ψ3(a)=τ3(T(a)) for all aξ1. Now we have,

    Now,infcpa+qbψ1(c)=infdT(pa+qb)τ1(d)=infd=d1+d2pT(a)+qT(b)τ1(d)[asTisagoodLT]=infd1pT(a)τ1(d1)infd2qT(b)τ1(d2)τ1(T(a))τ1(T(b))=ψ1(a)ψ1(b)
    infcpa+qbψ2(c)=infdT(pa+qb)τ2(d)=infd=d1+d2pT(a)+qT(b)τ2(d)[asTisagoodLT]=infd1pT(a)τ2(d1)infd2qT(b)τ2(d2)τ2(T(a))τ2(T(b))=ψ2(a)ψ2(b)
    andsupcpa+qbψ3(c)=supdT(pa+qb)τ3(d)=supd=d1+d2pT(a)+qT(b)τ3(d)[asTisagoodLT]=supd1pT(a)τ3(d1)supd2qT(b)τ3(d2)τ3(T(a))τ3(T(b))=ψ3(a)ψ3(b)

    Thus, T1(τ) is a PFSHS of ξ1.

    In algebraic structure, the algebraic operation of two elements gives a single element whereas in algebraic hyperstructure, algebraic hyperoperation of two elements gives a set. This is the main difference between algebraic operation and algebraic hyperoperation. When for aξ, pF; pa gives a singleton set {pa}, then hyper vector space is reduced to vector space. In this case, hyperoperative condition of picture fuzzy sub-hyperspace is reduced to the following. infcpaτ1(c)τ1(a), infcpaτ2(c)τ2(a) and supcpaτ3(c)τ3(a) for all aξ and for all pF.

    This implies, infc{pa}τ1(c)τ1(a), infc{pa}τ2(c)τ2(a) and supc{pa}τ3(c)τ3(a) for all aξ and for all pF.

    That is, τ1(pa)τ1(a), τ2(pa)τ2(a) and τ3(pa)τ3(a) for all aξ and for all pF.

    Thus, the conditions of PFSHS are reduced to the conditions of PFSS. Hence, the study of subspace under picture fuzzy environment is a particular case of the study of SHS under picture fuzzy environment i.e. in other words, the study of SHS under picture fuzzy environment is a generalization of the study of subspace under picture fuzzy environment. This study can be extended in other types of more complicated uncertain atmosphere (picture fuzzy interval valued atmosphere, spherical fuzzy atmosphere etc.) or to other types of algebraic hyperstructures (hypergroup, hypermodules etc.).

    Vector space/hyper vector space is an important type of algebraic structure/hyperstructure. Algebraic structure/hyper structure has a lot of applications in different areas of Computer Science such as error correction, coding theory etc. In our daily life, uncertainty occurs in every now and then based on human opinions. As an uncertainty handling tool, fuzzy set was invented by Zadeh [1]. Later on to handle higher level of uncertainties, intuitionistic fuzzy set was introduced by Atanassov [17] as an extension of fuzzy set and later on picture fuzzy set was initiated by Cuong [18] as an extension of intuitionistic fuzzy set. These provide enough motivation among the researchers to study algebraic structures/hyperstructures in fuzzy and advanced fuzzy environment. Vector space deals with two compositions namely internal composition and external composition. By internal composition, we simply mean addition of vectors and by external composition we simply mean scalar multiplication with vectors. In vector space, addition of two vectors produces a vector and scalar multiplication with vector produces vector. In case of hyper vector space, scalar composition with vector produces set of vectors. This is the main key operation in hyper vector space called hyperoperation. This hyperoperation can be linked with human life incident. Brides and grooms are human beings. As a result they are of same category. So brides together with grooms can be taken as field of scalars. Successors may come as a result of composition of bride and groom. Successors may have different genetic characteristics such as body colour, eye colour, height, blood type, intelligence level etc. inherited from their parents. For a finite hyper vector space, using hyperoperative conditions of PFSHS, a bride can choose a suitable groom for marriage out of a finite number of available grooms in order to produce genetically superior successors compared with him. A finite field over that field forms a finite hyper vector space, provided that hyperoperative composition is allowed in the field. In this case, there is no distinction between scalars and vectors i.e. in this case, scalars and vectors are same. It is necessary to mention that when hyperoperative conditions are applied as PFSHS conditions then the successors are genetically superior than their parents. Now our target is to select the best couple in order to produce genetically most superior successors. This selection of best couple with respect to a particular bride/groom follows a sequence of steps. Below we give an algorithm of best couple selection for a particular bride.

    Algorithm: Aim: To select the best couple for marriage with respect to a particular bride to produce genetically most superior successors.

    Input: A finite set of brides and grooms as field of scalars such that PFSHS conditions are satisfied.

    Output: The best couple for marriage with respect to a particular bride.

    The following steps are to be followed to reach the desired goal.

    Step 1: Collect data about hyperoperative composition of grooms with a particular bride from composition table.

    Step 2: Calculate the least picture fuzzy measurement for each set of successors using the data collected in picture fuzzy sense based on human opinions about a specific genetic characteristics of the possible successors.

    Step 3: Find the degrees of superiority of the successors.

    Step 4: Arrange the degrees of superiority in descending order.

    Step 5: Conclude about the selection of the best couple.

    It is to be noted that one can use the same algorithm pattern for best couple selection with respect to a particular groom.

    The proposed algorithm for best couple selection with respect to a particular bride/ groom can be depicted as a flowchart. Here flowchart for best couple selection with respect to a particular bride is shown in Figure 1.

    Figure 1.  Flowchart of groom selection for a particular bride.

    Illustration of Algorithm: Let us consider a finite field F={b1,b2,...,bn, g1,g2,...,gn}, where bi be the brides, gi be the grooms for i=1,2,...,n. Here gibk (for some k{1,2,...,n}) is the set of possible successors that may come from the groom gi (for i=1,2,...,n) and the bride bk. If zgibk then τ1(z) is the MPMS, τ2(z) is the MNeuMS and τ3(z) is the MNegMS of the successor z in connection with a specific genetic characteristics of z. Here the data about genetic characteristics is collected based on human opinions. Say l(k)i=infzgibkτ1(z)τ1(bk), m(k)i=infzgibkτ2(z)τ2(bk) and n(k)i=τ3(bk)supzgibkτ3(z) for i=1,2,3,...,n. Here l(k)i,m(k)i,n(k)i are all non-negative as PFSHS conditions are satisfied. Now degree of superiority of the possible successors that may come from the groom gi and the bride bk is s(k)i=l(k)i+m(k)in(k)i for i=1,2,...,n. Arranging s(k)i in descending order, we can conclude about groom selection for the bride bk. In the similar fashion, one can conclude about bride selection for a particular groom when selection is on the hand of groom.

    Now let us illustrate this method for n=2.

    Let us consider a finite set F={b1,b2,g1,g2} as a field, where b1,b2 be the brides; g1,g2 be the grooms. Then F over F forms a finite hyper vector space under the + and defined by

    Let us consider a PFS τ=(τ1,τ2,τ3) over F defined by τ={(b1,0.62,0.18,0.22), (b2,0.59,0.16,0.25),(g1,0.44,0.11,0.36),(g2,0.50,0.14,0.28)}.

    Clearly τ forms a PFSHS of ξ.

    Here the decision making problem can be viewed from four different angles.

    Case 1: When selection is on the hand of the bride b1.

    Since PFSHS conditions are satisfied therefore it can be written as

    infzg1b1τ1(z)τ1(b1), infzg1b1τ2(z)τ2(b1) and supzg1b1τ3(z)τ3(b1).

    That is, τ1(b1)τ1(b1), τ2(b1)τ2(b1) and τ3(b1)τ3(b1).

    So l(1)1=m(1)1=n(1)1=0. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the groom g1 and the bride b1 (when selection is on the hand of the bride b1) is s(1)1=l(1)1+m(1)1n(1)1=0.

    infzg2b1τ1(z)τ1(b1), infzg2b1τ2(z)τ2(b1) and supzg2b1τ3(z)τ2(b1).

    That is, τ1(b1)τ1(b1), τ2(b1)τ2(b1) and τ3(b1)τ3(b1).

    So l(1)2=m(1)2=n(1)2=0. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the groom g2 and the bride b1 (when selection is on the hand of the bride b1) is s(1)2=l(1)2+m(1)2n(1)2=0.

    Here the possible successors of b1 are genetically equivalent to b1. Since b1 is genetically improved (which is clear from picture fuzzy information of b1 related to some specific genetic characteristics), so b1 can choose any one groom for marriage out of the grooms g1 and g2.

    Case 2: When selection is on the hand of the bride b2.

    Since PFSHS conditions are satisfied therefore it can be written as

    infzg1b2τ1(z)τ1(b2), infzg1b2τ2(z)τ2(b2) and supzg1b2τ3(z)τ3(b2).

    That is, τ1(b2)τ1(b2), τ2(b2)τ2(b2) and τ3(b2)τ3(b2).

    So l(2)1=m(2)1=n(2)1=0. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the groom g1 and the bride b2 (when selection is on the hand of bride b2) is s(2)1=l(2)1+m(2)1n(2)1=0.

    infzg2b2τ1(z)τ1(b2), infzg2b2τ2(z)τ2(b2) and supzg2b2τ3(z)τ2(b2).

    That is, τ1(b2)τ1(b2), τ2(b2)τ2(b2) and τ3(b2)τ3(b2).

    So l(2)2=m(2)2=n(2)2=0. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the groom g2 and the bride b2 (when selection is on the hand of bride b2) is s(2)2=l(2)2+m(2)2n(2)2=0.

    Here the possible successors of b2 are genetically equivalent to b2. Since b2 is genetically improved (which is clear from picture fuzzy information of b2 related to some specific genetic characteristics), so b2 can choose any one groom for marriage out of the grooms g1 and g2.

    Case 3: When selection is on the hand of the groom g1.

    Since PFSHS conditions are satisfied therefore it can be written as

    infzb1g1τ1(z)τ1(g1), infzb1g1τ2(z)τ2(g1) and supzb1g1τ3(z)τ3(g1).

    That is, τ1(b1)τ1(g1), τ2(b1)τ2(g1) and τ3(b1)τ3(g1).

    That is, 0.620.44, 0.180.11 and 0.220.36.

    So l(1)1=0.620.44=0.18, m(1)1=0.180.11=0.07 and n(1)1=0.360.22=0.14. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the bride b1 and the groom g1 (when selection is on the hand of the groom g1) is s(1)1=l1+m1n1=0.18+0.070.14=0.11.

    infzb2g1τ1(z)τ1(g1), infzb2g1τ2(z)τ2(g1) and supzb2g1τ3(z)τ2(g1).

    That is, τ1(b2)τ1(g1), τ2(b2)τ2(g1) and τ3(b2)τ3(g1).

    That is, 0.590.44, 0.160.11 and 0.250.36.

    So l(1)2=0.590.44=0.15, m(1)2=0.160.11=0.05 and n(1)2=0.360.25=0.11. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the bride b2 and the groom g1 (when selection is on the hand of the groom g1) is s(1)2=l(1)2+m(1)2n(1)2=0.15+0.050.11=0.09.

    Since s(1)1>s(1)2, therefore the groom g1 can select the bride b1 for marriage out of the brides b1 and b2.

    Case 4: When selection is on the hand of the groom g2.

    Since PFSHS conditions are satisfied therefore it can be written as

    infzb1g2τ1(z)τ1(g2), infzb1g2τ2(z)τ2(g2) and supzb1g2τ3(z)τ3(g2).

    That is, τ1(b1)τ1(g2), τ2(b1)τ2(g2) and τ3(b1)τ3(g2).

    That is, 0.620.50, 0.180.14 and 0.220.28.

    So l(2)1=0.620.50=0.12, m(2)1=0.180.14=0.04 and n(2)1=0.280.22=0.06. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the bride b1 and the groom g2 (when selection is on the hand of groom g2) is s(2)1=l(2)1+m(2)1n(2)1=0.12+0.040.06=0.10.

    infzb2g2τ1(z)τ1(g2), infzb2g2τ2(z)τ2(g2) and supzb2g2τ3(z)τ3(g2).

    That is, τ1(b2)τ1(g2), τ2(b2)τ2(g2) and τ3(b2)τ3(g2).

    That is, 0.590.50, 0.160.14 and 0.250.28.

    So l(2)2=0.590.50=0.09, m(2)2=0.160.14=0.02 and n(2)2=0.280.25=0.03. Thus the degree of superiority of the possible successors/next generation as a result of hyperoperative composition of the groom b2 and the bride g2 (when selection is on the hand of groom g2) is s(2)2=l(2)2+m(2)2n(2)2=0.09+0.020.03=0.08.

    Since s(2)1>s(2)2, therefore the groom g2 can select the bride b1 for marriage out of the brides b1 and b2.

    So the best couple for marriage with respect to a particular bride/groom is listed below.

    When selection is on the hand of Selected best couple
    b1 any of b1g1 or b1g2
    b2 any of b2g1 or b2g2
    g1 g1b1
    g2 g2b1

    Limitations of the Proposed Method: Note that when for a particular bride bk (for some k{1,2,...,n}), all s(k)i=0 for i=1,2,...,n; then possible successors are genetically equivalent to bk. In this case, our algorithm does not work. For decision making, we have to look at the picture fuzzy information of bk related to some specific genetic characteristics of bk. Moreover, for large domain, n becomes large and the composition table becomes little difficult. So careful data handling is to be done in order to satisfy PFSHS conditions. This method can only be used in real life applications where composition concept arises in practical situation. These are some of the limitations of our proposed method. Despite these limitations, we think that our method will add a new dimension not only in the field of decision making but also in the field of algebra because decision making has been done here from algebraic point of view.

    In this paper, we have introduced PFSHS of a HVS and studied some elementary properties in connection with some basic operations (intersection, union, Cartesian product etc.) on picture fuzzy sets. We have discussed the effect of good LT on PFSHS. We have proved that image of a PFSHS is a PFSHS under bijective good LT and inverse image of a PFSHS is a PFSHS under good LT. We have initiated the notion of PFLT with respect to some pre-assumed PFSHS. We have shown that linear combination of two PFLTs is a PFLT. This proposition merges two results: (1) The sum of two PFLTs is a PFLT and (ii) scalar multiplication with PFLT is a PFLT. Finally, we have presented an application of PFSHS condition in decision making problem. As a result of our study, the researchers will able to justify the validity of the results on sub-hyperspace as a certain case of our study. This study can be treated as the study of a special type of advanced fuzzy hyper algebraic structure. This study opens a new window for the researchers who are interested to study more about sub-hyperspace in picture fuzzy setting. Moreover, investigation about sub-hyperspace under some other types of uncertain environment will be easy for the researchers who will go through this research work. Researchers will be able to solve different types of transportation problems, linear programming problems using picture fuzzy set algorithm in those cases where fuzzy set algorithm is unable to provide fruitful solution. Our method is so much interesting due to its application in decision making field from algebraic point of view.

    This work is supported by Research Council Faroe Islands and University of the Faroe Islands for the third author. The authors are grateful to the anonymous referees for careful checking of the details and for giving helpful comments towards the improvement of the overall presentation of the paper.

    The authors declare that they have no conflict of interest.



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