Research article Special Issues

A novel binary genetic differential evolution optimization algorithm for wind layout problems

  • Received: 23 November 2023 Revised: 10 January 2024 Accepted: 23 January 2024 Published: 05 February 2024
  • This paper addresses the increasingly critical issue of environmental optimization in the context of rapid economic development, with a focus on wind farm layout optimization. As the demand for sustainable resource management, climate change mitigation, and biodiversity conservation rises, so does the complexity of managing environmental impacts and promoting sustainable practices. Wind farm layout optimization, a vital subset of environmental optimization, involves the strategic placement of wind turbines to maximize energy production and minimize environmental impacts. Traditional methods, such as heuristic approaches, gradient-based optimization, and rule-based strategies, have been employed to tackle these challenges. However, they often face limitations in exploring the solution space efficiently and avoiding local optima. To advance the field, this study introduces LSHADE-SPAGA, a novel algorithm that combines a binary genetic operator with the LSHADE differential evolution algorithm, effectively balancing global exploration and local exploitation capabilities. This hybrid approach is designed to navigate the complexities of wind farm layout optimization, considering factors like wind patterns, terrain, and land use constraints. Extensive testing, including 156 instances across different wind scenarios and layout constraints, demonstrates LSHADE-SPAGA's superiority over seven state-of-the-art algorithms in both the ability of jumping out of the local optima and solution quality.

    Citation: Yanting Liu, Zhe Xu, Yongjia Yu, Xingzhi Chang. A novel binary genetic differential evolution optimization algorithm for wind layout problems[J]. AIMS Energy, 2024, 12(1): 321-349. doi: 10.3934/energy.2024016

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  • This paper addresses the increasingly critical issue of environmental optimization in the context of rapid economic development, with a focus on wind farm layout optimization. As the demand for sustainable resource management, climate change mitigation, and biodiversity conservation rises, so does the complexity of managing environmental impacts and promoting sustainable practices. Wind farm layout optimization, a vital subset of environmental optimization, involves the strategic placement of wind turbines to maximize energy production and minimize environmental impacts. Traditional methods, such as heuristic approaches, gradient-based optimization, and rule-based strategies, have been employed to tackle these challenges. However, they often face limitations in exploring the solution space efficiently and avoiding local optima. To advance the field, this study introduces LSHADE-SPAGA, a novel algorithm that combines a binary genetic operator with the LSHADE differential evolution algorithm, effectively balancing global exploration and local exploitation capabilities. This hybrid approach is designed to navigate the complexities of wind farm layout optimization, considering factors like wind patterns, terrain, and land use constraints. Extensive testing, including 156 instances across different wind scenarios and layout constraints, demonstrates LSHADE-SPAGA's superiority over seven state-of-the-art algorithms in both the ability of jumping out of the local optima and solution quality.



    Let H,K be complex or real separable Hilbert spaces and n a positive integer. As usual, the symbols Pn(H) and IH stand for the set of all rank-n self-adjoint projections on H, and the identity operator on H, respectively. For S,TPn(H), we say S is orthogonal to T iff ST=0 and the quantity Tr(ST) is the transition probability between S,T. Plainly, ST is equivalent to Tr(ST)=0. If uH is a unit vector, then the rank-1 projection onto span{u} will be denoted by uu. The transition probability associated with a pair of rank-1 projections (pure states) is the commonly used concept in quantum theory. We call a family {Si}Pn(H) a complete orthogonal system of rank-n projections (briefly, COSPn) iff

    SiSj whenever ij.

    ● There is no rank-1 projection T orthogonal to each Si.

    The celebrated Wigner's theorem [1, pp.251–254] states that if ϕ:P1(H)P1(H) is a bijection satisfying

    Tr(ϕ(S)ϕ(T))=Tr(ST),S,TP1(H), (1.1)

    equivalently, if ϕ preserves the transition probability between S and T, then there exists a unitary or an anti-unitary U:HH such that ϕ(A)=UAU. Recently, there has been considerable interest in improving and reproving this vital result in many ways (referred to in [2,3,4,5,6,7]).

    Wigner's theorem also serves as a frequently used tool for investigating the symmetries in some mathematical structures of quantum mechanics. Suppose that ϕ is a bijection on the set of all observables/the state space/the effect algebra, and such a map preserves a certain property/relation/operation relevant in quantum mechanics. The given problem is to characterize the form of such maps (symmetries), and a classical approach to this problem is to first show that ϕ preserves the rank-1 projections and the corresponding transition probability. This is the crucial step of the proof. Applying Wigner's theorem, one may immediately see that the restriction of ϕ to P1(H) has a nice behavior. Then the final step to prove that ϕ takes the desired form on the entire quantum structure is usually considered as an easier part of the proof. The interested readers are referred to [8, Chapter 2] and references therein for more examples of this approach and some background for the so-called preservers problems.

    When using the above method, sometimes we may not ensure that ϕ maps P1(H) into itself, and quite often we merely know that it preserves the zero-transition probability. This motivates us to search for a stronger version of the classical Wigner's theorem. The main aim of this paper is to provide the generalizations of Wigner's theorem in which instead of assuming that ϕ maps P1(H) into itself, we assume that ϕ maps P1(H) into Pn(K).

    Theorem 1.1. If ϕ:P1(H)Pn(K) is a map satisfying

    Tr(ϕ(S)ϕ(T))=nTr(ST),S,TP1(H), (1.2)

    then there exists a collection {V1,,Vn} of linear or conjugate linear isometries from H into K with mutually orthogonal ranges, such that

    ϕ(A)=ni=1ViAVi,AP1(H).

    Notice that the property (1.2) is equivalent to the following condition:

    ϕ(S)ϕ(T)HS=nSTHS,S,TP1(H),

    where HS represents the Hilbert–Schmidt norm. Namely, our result describes the general form of maps from P1(H) into Pn(K) multiplying n times the distance induced by this special norm. We point out that several papers [9,10] studied the isometries of Pn(H) with respect to the operator norm.

    For the case of dimH3, Uhlhorn [11] significantly generalized Wigner's theorem by replacing the assumption (1.1) with a weaker one: Tr(ST)=0Tr(ϕ(S)ϕ(T))=0. Uhlhorn's result has been further improved in [12,13]: It is proved that the bijectivity assumption can be relaxed when dimH<. Unfortunately, when dimH=, it is shown in [14] that there exist injective maps preserving orthogonality in both directions, which behave quite wildly. Thus, an additional hypothesis will be needed in the infinite-dimensional case.

    Theorem 1.2. Let dimH3. If ϕ:P1(H)Pn(K) is a map that sends each complete orthogonal system of rank-1 projections to some complete orthogonal system of rank-n projections, then there exists a collection {V1,,Vn} of linear or conjugate linear isometries from H into K, which have mutually orthogonal ranges and satisfy ni=1ViVi=I, such that

    ϕ(A)=ni=1ViAVi,AP1(H). (1.3)

    If 3dimH< and dimK=ndimH, then a map ϕ:P1(H)Pn(K) that preserves orthogonality only in one direction automatically sends each COSP1 to some COSPn. Therefore, a generalization (without bijectivity either) of Uhlhorn's theorem in matrix algebra is a direct consequence of Theorem 1.2.

    Corollary 1.3. Let 3dimH< and dimK=ndimH. If ϕ:P1(H)Pn(K) is a map that preserves orthogonality in one direction, then ϕ has the form (1.3).

    In what follows, we denote by C(H), F(H), and Fs(H) the set of compact operators, finite-rank operators, and finite-rank self-adjoint operators on H. The following lemma will be used to prove Theorem 1.1.

    Lemma 2.1. If ϕ:Fs(H)Fs(K) is a linear map that sends rank-1 projections to rank-n projections and satisfies

    Tr(ϕ(S)ϕ(T))=nTr(ST),S,TFs(H), (2.1)

    then there exists a collection {V1,,Vn} of linear or conjugate linear isometries from H into K with mutually orthogonal ranges, such that

    ϕ(A)=ni=1ViAVi,AFs(H).

    To prove Lemma 2.1, we need the following lemmas. For S,TFs(H), we write ST if TS is positive.

    Lemma 2.2. Let ϕ:Fs(H)Fs(K) be a linear map that preserves projections. If S,TFs(H) are projections with ST, then ϕ(S)ϕ(T).

    Proof. Since S,T are projections with ST, there exists some projection R orthogonal to T, such that S=T+R. Thus, ϕ(S)=ϕ(T)+ϕ(R)ϕ(T).

    Lemma 2.3. (see [15, Theorem 1.9.1]) Let M be a dense subspace of a normed space V, and W a Banach space. If ϕ:MW is a continuous linear map, then ϕ has a unique continuous linear extension ϕ:VW.

    Proof of Lemma 2.1. By Eq (2.1), we see that ϕ sends orthogonal rank-1 projections to orthogonal rank-n projections. Clearly, any finite-rank projection is the sum of mutually orthogonal rank-1 projections. Consequently, ϕ preserves the projections.

    Assume that the underlying space H is complex. Extend ϕ to a complex linear map from F(H) into F(K) by setting

    ˜ϕ(A+iB):=ϕ(A)+iϕ(B),A,BFs(H).

    Let A=iαiPi, αiR, PiP1(H), denote the spectral decomposition of any operator AFs(H). Then ˜ϕ(Pi)˜ϕ(Pj)=0 for each ij, and hence ˜ϕ(A2)=˜ϕ(A)2. Replacing A by A+B, with A,BFs(H), we obtain that ˜ϕ(AB+BA)=˜ϕ(A)˜ϕ(B)+˜ϕ(B)˜ϕ(A). Then it follows that

    ˜ϕ((A+iB)2)=˜ϕ(A2)˜ϕ(B2)+i˜ϕ(AB+BA)=˜ϕ(A)2˜ϕ(B)2+i(˜ϕ(A)˜ϕ(B)+˜ϕ(B)˜ϕ(A))=(˜ϕ(A)+i˜ϕ(B))2=˜ϕ(A+iB)2.

    This implies that ˜ϕ is a Jordan homomorphism. Since ˜ϕ preserves the self-adjoint operators, we infer that ˜ϕ is a (continuous) Jordan - homomorphism. It is known that F(H) is dense in the C - algebra C(H). By Lemma 2.3, ˜ϕ can be uniquely extended to a Jordan - homomorphism from C(H) into C(K). According to [8, Theorem A.6], each Jordan - homomorphism of the C - algebra is a direct sum of a -antihomomorphism and a - homomorphism. Every - homomorphism of C(H) is in fact a direct sum of inner homomorphisms (see [16, Theorem 10.4.7]). Then ˜ϕ has the asserted form.

    The case when H is real demands an other approach (this idea is borrowed from [17, Theorem 2.2] below). Assume that {ui}iΩ is an orthonormal basis for H and denote rngϕ(uiui)=Ki, iΩ. For any i,jΩ with ij, since [(ui+uj)(ui+uj)]/2 is a projection with range lying within that of uiui+ujuj, it follows by Lemma 2.2 that

    12ϕ((uiuj+ujui)+(uiui+ujuj))IKiKj0.

    Therefore, we may write

    Pij=ϕ(uiuj+ujui)=[PiiPijPjiPjj]0

    for some linear operator Pij:KjKi. For any nonzero αR, consider

    Q1=(α2+1)1(α2u1u1+α(u1u2+u2u1)+u2u2)P1(H),Q2=(α2+1)1(u1u1α(u1u2+u2u1)+α2u2u2)P1(H).

    By directly computing, Q1Q2=0. It follows that

    0=(α2+1α)2ϕ(Q1)ϕ(Q2)=(ϕ(αu1u1+1αu2u2)+P12)( ϕ(1αu1u1+αu2u2)P12)=([αIK1001αIK2]0+P12)([1αIK100αIK2]0P12)=[IK100IK2]0P212[αP11αP121αP211αP22]0+[1αP11αP121αP21αP22]0=[IK100IK2]0P212[(α1α)P1100(1αα)P22]0.

    Because this equation holds true for any nonzero αR, we see that P11 and P22 are zeroes. Hence, we obtain

    P12P21=IK1 and P21P12=IK2.

    It follows that P12=P211=P21 and we have similar conclusions for all Pij.

    Since all Ki are isomorphic to Rn, there exists an isomorphism from HRn to ΩKi, given by iuiηi(ηi). Write K=(HRn)Ks and replace ϕ by the mapping

    A(IK1(iΩ,i1P1i)IKs)ϕ(A)(IK1(iΩ,i1P1i1)IKs)

    such that

    ϕ(u1ui+uiu1)=[(u1ui+uiu1)IRn]0Ks,iΩ.

    We are going to prove that

    ϕ(uiuj+ujui)=[(uiuj+uiuj)IRn]0Ks wheneveri,jΩwithij.

    To see this, let Z=[(u1+ui+uj)(u1+ui+uj)]/3. Then Z is a rank-1 projection such that, up to unitary similarity, ϕ(Z) is equal to a direct sum of 0 and

    Y=31[IRnIRnIRnIRnIRnPijIRnPij1IRn].

    As Y2=Y, it follows that IRn+2Pij=3Pij. Thus Pij=Pij1=IRn.

    Since spanR{uiuj+ujui:i,jΩ} is dense in Fs(H) when H is a real Hilbert space, we can prove that

    ϕ(A)=U[(AIRn)0Ks]U,AFs(H),

    where U:KK is a unitary. We arrive at the conclusion.

    Proof of Theorem 1.1. Since the whole Fs(H) is real linearly generated by P1(H), we may extend ϕ to a real-linear map ˜ϕ:Fs(H)Fs(K) by setting

    ˜ϕ(iλiSi):=iλiϕ(Si)

    where {λi}R and {Si}P1(H) are finite subsets. We claim that ˜ϕ is well-defined. Assume that iλiSi=jμjTj, {μj}R, {Tj}P1(H). Then for each AP1(H), it follows by Eq (1.2) that

    Tr(iλiϕ(Si)ϕ(A))=iλiTr(ϕ(Si)ϕ(A))=iλinTr(SiA)=nTr(iλiSiA)=nTr(jμjTjA)=jμjnTr(TjA)=jμjTr(ϕ(Tj)ϕ(A))=Tr(jμjϕ(Tj)ϕ(A)).

    This implies that

    Tr((iλiϕ(Si)jμjϕ(Tj))ϕ(A))=0.

    Based on the linearity of the function Tr, we can replace ϕ(A) by its linear combination. Then we obtain

    Tr((iλiϕ(Si)jμjϕ(Tj))(iλiϕ(Si)jμjϕ(Tj)))=0.

    Since the square of Hermitian operator (iλiϕ(Si)jμjϕ(Tj))2 is positive with zero trace, we deduce that

    (iλiϕ(Si)jμjϕ(Tj))2=0=iλiϕ(Si)jμjϕ(Tj).

    It means that ˜ϕ is well-defined. Then the form of this linear map ˜ϕ is given by Lemma 2.1.

    Proof of Theorem 1.2. First, let us recall Gleason's theorem [18]. A positive and trace-class operator σ:HH with Tr(σ)=1 is called a density operator. We restate Gleason's theorem as follows: Suppose that dimH3 and f:P1(H)[0,1] is a function such that for each complete orthogonal system of rank-1 projections {Si}P1(H), one has

    if(Si)=1.

    Then there is a density operator σ:HH for which

    f(S)=Tr(σS),SP1(H).

    To prove Theorem 1.2, we need to choose an arbitrary density operator ϱ:KK and define the function fϱ:P1(H)[0,1] by

    fϱ(S):=Tr(ϱϕ(S)),SP1(H).

    It follows from our assumption that Gleason's theorem can be used. Therefore, for each density operator ϱ:KK, there exists a density operator σ:HH such that

    fϱ(S)=Tr(σS),SP1(H).

    In particular, pick ϱ=ϕ(T)/n for some fixed TP1(H). Then we obtain

    fϱ(S)=1nTr(ϕ(T)ϕ(S))=Tr(σTS),SP1(H),

    where σT is the density operator corresponding to T. Taking S=T, we infer that

    Tr(σTT)=1.

    It is easy to verify that if u is a unit vector such that T=uu, then

    Tr(σTT)=σTu,u.

    As 0σTI, it follows by the operator theory that σTu=u. Therefore, 1 is an eigenvalue of σT and u belongs to the corresponding eigenspace. Under the decomposition H=span{u}{u}, the operator σT has the following matrix representation:

    σT=[100X],

    where X is the positive operator acting on {u} with zero trace. Thus, X=0, which means σT=T.

    Hence, for each SP1(H), we have Tr(ϕ(S)ϕ(T))=nTr(ST). Since T was chosen arbitrarily, we deduce that ϕ multiplies n times the transition probability. Then Theorem 1.1 tells us the form of the map ϕ.

    The conclusion in Theorem 1.2 does not hold when dimH=2, as demonstrated in the following example: In fact, we can identify H with C2 and hence F(H)=M2(C), the set of 2×2 complex matrices. All the rank-1 projections in M2(C) are in 1-to-1 correspondence with the unit vectors in the Bloch sphere in R3, i.e.: $

    P1(C2)={21[1+x1x2+ix3x2ix31x1]:x1,x2,x3R with x21+x22+x23=1}.

    It is straightforward to compute the orthogonal complement of

    A=21[1+x1x2+ix3x2ix31x1] is IA=21[1x1x2ix3x2+ix31+x1].

    Consider the bijective transformation ϕ:P1(C2)P1(C2), which fix all rank-1 projections, but change the role of [1000] and [0001]. Obviously, the only COSP1 in M2(C) that contains [1000] is {[1000],[0001]} and hence ϕ preserves orthogonality. However, this discontinuous transformation ϕ can not be extended to any linear transformation (in fact, any Jordan - homomorphism also) on the whole matrix space M2(C).

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This study was funded by Fundamental Research Funds for the Central Universities of China (Grant No. 2572022DJ07).

    The authors declare there is no conflicts of interest.



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