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
[1] | Yaguo Guo, Shilin Yang . Projective class rings of the category of Yetter-Drinfeld modules over the 2-rank Taft algebra. Electronic Research Archive, 2023, 31(8): 5006-5024. doi: 10.3934/era.2023256 |
[2] | Xin Lu, Jinbang Yang, Kang Zuo . Strict Arakelov inequality for a family of varieties of general type. Electronic Research Archive, 2022, 30(7): 2643-2662. doi: 10.3934/era.2022135 |
[3] | Liu Yang, Yuehuan Zhu . Second main theorem for holomorphic curves on annuli with arbitrary families of hypersurfaces. Electronic Research Archive, 2024, 32(2): 1365-1379. doi: 10.3934/era.2024063 |
[4] | Vladimir Lazić, Fanjun Meng . On Nonvanishing for uniruled log canonical pairs. Electronic Research Archive, 2021, 29(5): 3297-3308. doi: 10.3934/era.2021039 |
[5] | Lie Fu, Victoria Hoskins, Simon Pepin Lehalleur . Motives of moduli spaces of rank 3 vector bundles and Higgs bundles on a curve. Electronic Research Archive, 2022, 30(1): 66-89. doi: 10.3934/era.2022004 |
[6] | Yimou Liao, Tianxiu Lu, Feng Yin . A two-step randomized Gauss-Seidel method for solving large-scale linear least squares problems. Electronic Research Archive, 2022, 30(2): 755-779. doi: 10.3934/era.2022040 |
[7] | Rongmin Zhu, Tiwei Zhao . The construction of tilting cotorsion pairs for hereditary abelian categories. Electronic Research Archive, 2025, 33(5): 2719-2735. doi: 10.3934/era.2025120 |
[8] | Jun Guo, Yanchao Shi, Weihua Luo, Yanzhao Cheng, Shengye Wang . Exponential projective synchronization analysis for quaternion-valued memristor-based neural networks with time delays. Electronic Research Archive, 2023, 31(9): 5609-5631. doi: 10.3934/era.2023285 |
[9] | Frédéric Campana . Algebraicity of foliations on complex projective manifolds, applications. Electronic Research Archive, 2022, 30(4): 1187-1208. doi: 10.3934/era.2022063 |
[10] | Yu Wang . Bi-shifting semantic auto-encoder for zero-shot learning. Electronic Research Archive, 2022, 30(1): 140-167. doi: 10.3934/era.2022008 |
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,T∈Pn(H), we say S is orthogonal to T iff ST=0 and the quantity Tr(ST) is the transition probability between S,T. Plainly, S⊥T is equivalent to Tr(ST)=0. If u∈H is a unit vector, then the rank-1 projection onto span{u} will be denoted by u⊗u. 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
● Si⊥Sj whenever i≠j.
● 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,T∈P1(H), | (1.1) |
equivalently, if ϕ preserves the transition probability between S and T, then there exists a unitary or an anti-unitary U:H→H 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,T∈P1(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)=n∑i=1ViAV∗i,A∈P1(H). |
Notice that the property (1.2) is equivalent to the following condition:
‖ϕ(S)−ϕ(T)‖HS=√n‖S−T‖HS,S,T∈P1(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 dimH≥3, Uhlhorn [11] significantly generalized Wigner's theorem by replacing the assumption (1.1) with a weaker one: Tr(ST)=0⇔Tr(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 dimH≥3. 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=1ViV∗i=I, such that
ϕ(A)=n∑i=1ViAV∗i,A∈P1(H). | (1.3) |
If 3≤dimH<∞ 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 3≤dimH<∞ 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,T∈Fs(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)=n∑i=1ViAV∗i,A∈Fs(H). |
To prove Lemma 2.1, we need the following lemmas. For S,T∈Fs(H), we write S≤T if T−S is positive.
Lemma 2.2. Let ϕ:Fs(H)→Fs(K) be a linear map that preserves projections. If S,T∈Fs(H) are projections with S≥T, then ϕ(S)≥ϕ(T).
Proof. Since S,T are projections with S≥T, 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:M→W is a continuous linear map, then ϕ has a unique continuous linear extension ϕ′:V→W.
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,B∈Fs(H). |
Let A=∑iαiPi, αi∈R, Pi∈P1(H), denote the spectral decomposition of any operator A∈Fs(H). Then ˜ϕ(Pi)˜ϕ(Pj)=0 for each i≠j, and hence ˜ϕ(A2)=˜ϕ(A)2. Replacing A by A+B, with A,B∈Fs(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}i∈Ω is an orthonormal basis for H and denote rngϕ(ui⊗ui)=Ki, i∈Ω. For any i,j∈Ω with i≠j, since [(ui+uj)⊗(ui+uj)]/2 is a projection with range lying within that of ui⊗ui+uj⊗uj, it follows by Lemma 2.2 that
12ϕ((ui⊗uj+uj⊗ui)+(ui⊗ui+uj⊗uj))≤IKi⊕Kj⊕0. |
Therefore, we may write
Pij=ϕ(ui⊗uj+uj⊗ui)=[P′iiP′ijP′jiP′jj]⊕0 |
for some linear operator P′ij:Kj→Ki. For any nonzero α∈R, consider
Q1=(α2+1)−1(α2u1⊗u1+α(u1⊗u2+u2⊗u1)+u2⊗u2)∈P1(H),Q2=(α2+1)−1(u1⊗u1−α(u1⊗u2+u2⊗u1)+α2u2⊗u2)∈P1(H). |
By directly computing, Q1Q2=0. It follows that
0=(α2+1α)2ϕ(Q1)ϕ(Q2)=(ϕ(αu1⊗u1+1αu2⊗u2)+P12)( ϕ(1αu1⊗u1+αu2⊗u2)−P12)=([αIK1001αIK2]⊕0+P12)([1αIK100αIK2]⊕0−P12)=[IK100IK2]⊕0−P212−[αP′11αP′121αP′211αP′22]⊕0+[1αP′11αP′121αP′21αP′22]⊕0=[IK100IK2]⊕0−P212−[(α−1α)P′1100(1α−α)P′22]⊕0. |
Because this equation holds true for any nonzero α∈R, we see that P′11 and P′22 are zeroes. Hence, we obtain
P′12P′21=IK1 and P′21P′12=IK2. |
It follows that P′12=P′21−1=P′21∗ and we have similar conclusions for all Pij.
Since all Ki are isomorphic to Rn, there exists an isomorphism from H⊗Rn to ⨁ΩKi, given by ∑iui⊗ηi→(⋯ηi⋯). Write K=(H⊗Rn)⊕Ks and replace ϕ by the mapping
A→(IK1⊕(⨁i∈Ω,i≠1P′1i)⊕IKs)ϕ(A)(IK1⊕(⨁i∈Ω,i≠1P′1i−1)⊕IKs) |
such that
ϕ(u1⊗ui+ui⊗u1)=[(u1⊗ui+ui⊗u1)⊗IRn]⊕0Ks,i∈Ω. |
We are going to prove that
ϕ(ui⊗uj+uj⊗ui)=[(ui⊗uj+ui⊗uj)⊗IRn]⊕0Ks wheneveri,j∈Ωwithi≠j. |
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=3−1[IRnIRnIRnIRnIRnP′ijIRnP′ij−1IRn]. |
As Y2=Y, it follows that IRn+2P′ij=3P′ij. Thus P′ij=P′ij−1=IRn.
Since spanR{ui⊗uj+uj⊗ui:i,j∈Ω} is dense in Fs(H) when H is a real Hilbert space, we can prove that
ϕ(A)=U[(A⊗IRn)⊕0Ks]U∗,A∈Fs(H), |
where U:K→K 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 A∈P1(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:H→H with Tr(σ)=1 is called a density operator. We restate Gleason's theorem as follows: Suppose that dimH≥3 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:H→H for which
f(S)=Tr(σS),S∈P1(H). |
To prove Theorem 1.2, we need to choose an arbitrary density operator ϱ:K→K and define the function fϱ:P1(H)→[0,1] by
fϱ(S):=Tr(ϱϕ(S)),S∈P1(H). |
It follows from our assumption that Gleason's theorem can be used. Therefore, for each density operator ϱ:K→K, there exists a density operator σ:H→H such that
fϱ(S)=Tr(σS),S∈P1(H). |
In particular, pick ϱ=ϕ(T)/n for some fixed T∈P1(H). Then we obtain
fϱ(S)=1nTr(ϕ(T)ϕ(S))=Tr(σTS),S∈P1(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=u⊗u, then
Tr(σTT)=⟨σTu,u⟩. |
As 0≤σT≤I, 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 S∈P1(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)={2−1[1+x1x2+ix3x2−ix31−x1]:x1,x2,x3∈R with x21+x22+x23=1}. |
It is straightforward to compute the orthogonal complement of
A=2−1[1+x1x2+ix3x2−ix31−x1] is I−A=2−1[1−x1−x2−ix3−x2+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.
[1] |
Abido MA (2003) Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Trans Power Syst 18: 1529–1537. https://doi.org/10.1109/TPWRS.2003.818693 doi: 10.1109/TPWRS.2003.818693
![]() |
[2] |
Lei ZY, Gao SC, Zhang ZM, et al. (2023) A chaotic local search-based particle swarm optimizer for large-scale complex wind farm layout optimization. IEEE/CAA J Autom Sin 10: 1168–1180. https://doi.org/10.1109/JAS.2023.123387 doi: 10.1109/JAS.2023.123387
![]() |
[3] |
Lei ZY, Gao SC, Wang YR, et al. (2022) An adaptive replacement strategy-incorporated particle swarm optimizer for wind farm layout optimization. Energy Convers Manage 269: 116174. https://doi.org/10.1016/j.enconman.2022.116174 doi: 10.1016/j.enconman.2022.116174
![]() |
[4] |
Lei ZY, Gao SC, Zhang ZM, et al. (2022) MO4: A many-objective evolutionary algorithm for protein structure prediction. IEEE Trans Evol Comput 26: 417–430. https://doi.org/10.1109/TEVC.2021.3095481 doi: 10.1109/TEVC.2021.3095481
![]() |
[5] |
Wang YR, Yu Y, Cao SY, et al. (2020) A review of applications of artificial intelligent algorithms in wind farms. Artif Intell Rev 53: 3447–3500. https://doi.org/10.1007/s10462-019-09768-7 doi: 10.1007/s10462-019-09768-7
![]() |
[6] |
Kiehbadroudinezhad M, Merabet M, Rajabipour A, et al. (2020) Optimization of wind/solar energy microgrid by division algorithm considering human health and environmental impacts for power-water cogeneration. Energy Convers Manage 252: 115064. https://doi.org/10.1016/j.enconman.2021.115064 doi: 10.1016/j.enconman.2021.115064
![]() |
[7] |
Reddy SR (2020) Wind farm layout optimization (WindFLO): An advanced framework for fast wind farm analysis and optimization. Appl Energy 269: 115090. https://doi.org/10.1016/j.apenergy.2020.115090 doi: 10.1016/j.apenergy.2020.115090
![]() |
[8] |
Liu ZQ, Peng J, Hua X, et al. (2021) Wind farm optimization considering non-uniformly distributed turbulence intensity. Sustainable Energy Technol Assess 43: 100970. https://doi.org/10.1016/j.seta.2020.100970 doi: 10.1016/j.seta.2020.100970
![]() |
[9] |
Gualtieri G (2020) Comparative analysis and improvement of grid-based wind farm layout optimization. Energy Convers Manage 208: 112593. https://doi.org/10.1016/j.enconman.2020.112593 doi: 10.1016/j.enconman.2020.112593
![]() |
[10] |
Moreno SR, Pierezan J, dos Santos Coelho L, et al. (2021) Multi-objective lightning search algorithm applied to wind farm layout optimization. Energy 216: 119214. https://doi.org/10.1016/j.energy.2020.119214 doi: 10.1016/j.energy.2020.119214
![]() |
[11] |
Beşkirli M, Koç İ, Haklı H, et al. (2018) A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm. Renewable Energy 121: 301–308. https://doi.org/10.1016/j.renene.2017.12.087 doi: 10.1016/j.renene.2017.12.087
![]() |
[12] |
Nash R, Nouri R, Vasel-Be-Hagh A (2021) Wind turbine wake control strategies: A review and concept proposal. Energy Convers Manage 245: 114581. https://doi.org/10.1016/j.enconman.2021.114581 doi: 10.1016/j.enconman.2021.114581
![]() |
[13] |
Lee CY, Hasegawa H, Gao SC (2022) Complex-valued neural networks: A comprehensive survey. IEEE/CAA J Autom Sin 9: 1406–1426. https://doi.org/10.1109/JAS.2022.105743 doi: 10.1109/JAS.2022.105743
![]() |
[14] |
Wang RL, Lei ZZ, Zhang ZM, et al. (2022) Dendritic convolutional neural network. IEEJ Trans Electr Electron Eng 17: 302–304. https://doi.org/10.1002/tee.23513 doi: 10.1002/tee.23513
![]() |
[15] |
Yu Y, Lei ZZ, Wang YR, et al. (2022) Improving dendritic neuron model with dynamic scale-free network-based differential evolution. IEEE/CAA J Autom Sin 9: 99–110. https://doi.org/10.1109/JAS.2021.1004284 doi: 10.1109/JAS.2021.1004284
![]() |
[16] |
Garcia Marquez FP, Peinado Gonzalo A (2022) A comprehensive review of artificial intelligence and wind energy. Arch Comput Methods Eng 29: 2935–2958. https://doi.org/10.1007/s11831-021-09678-4 doi: 10.1007/s11831-021-09678-4
![]() |
[17] | Lei ZY, Gao SC, Hasegawa H, et al. (2023) Fully complex-valued gated recurrent neural network for ultrasound imaging. IEEE Trans Neural Networks Learn Syst, 1–14. https://doi.org/10.1109/TNNLS.2023.3282231 |
[18] |
Yu XB, Lu YC (2023) Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization. Energy 284: 129300. https://doi.org/10.1016/j.energy.2023.129300 doi: 10.1016/j.energy.2023.129300
![]() |
[19] |
Bai FY, Ju XL, Wang SY, et al. (2022) Wind farm layout optimization using adaptive evolutionary algorithm with monte carlo tree search reinforcement learning. Energy Convers Manage 252: 115047. https://doi.org/10.1016/j.enconman.2021.115047 doi: 10.1016/j.enconman.2021.115047
![]() |
[20] |
Asaah P, Hao LL, Ji J (2021) Optimal placement of wind turbines in wind farm layout using particle swarm optimization. J Mod Power Syst Clean Energy 9: 367–375. https://doi.org/10.35833/MPCE.2019.000087 doi: 10.35833/MPCE.2019.000087
![]() |
[21] |
Reddy SR (2021) An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization. Renewable Energy 165: 162–173. https://doi.org/10.1016/j.renene.2020.10.076 doi: 10.1016/j.renene.2020.10.076
![]() |
[22] |
Mittal P, Mitra K (2020) In search of flexible and robust wind farm layouts considering wind state uncertainty. J Cleaner Prod 248: 119195. https://doi.org/10.1016/j.jclepro.2019.119195 doi: 10.1016/j.jclepro.2019.119195
![]() |
[23] |
Chen K, Song MX, Zhang X, et al. (2016) Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm. Renewable Energy 96: 676–686. https://doi.org/10.1016/j.renene.2016.05.018 doi: 10.1016/j.renene.2016.05.018
![]() |
[24] |
Guirguis D, Romero DA, Amon CH (2016) Toward efficient optimization of wind farm layouts: Utilizing exact gradient information. Appl Energy 179: 110–123. https://doi.org/10.1016/j.apenergy.2016.06.101 doi: 10.1016/j.apenergy.2016.06.101
![]() |
[25] |
Rehman S, Khan SA, Alhems LM (2020) A rule-based fuzzy logic methodology for multi-criteria selection of wind turbines. Sustainability 12: 8467. https://doi.org/10.3390/su12208467 doi: 10.3390/su12208467
![]() |
[26] |
Grady S, Hussaini M, Abdullah MM (2005) Placement of wind turbines using genetic algorithms. Renewable Energy 30: 259–270. https://doi.org/10.1016/j.renene.2004.05.007 doi: 10.1016/j.renene.2004.05.007
![]() |
[27] |
Zhong L, Sui QY, Yu JTY, et al. (2023) Elite-of-the-elites driven five-layered gravitational search algorithm for optimization. IEEJ Trans Electr Electron Eng 18: 1958–1960. https://doi.org/10.1002/tee.23921 doi: 10.1002/tee.23921
![]() |
[28] |
Sui QY, Zhong L, Yu JTY, et al. (2023) Particle swarm optimization with average individuals distance-incorporated exploitation. IEEJ Trans Electr Electron Eng 18: 1722–1724. https://doi.org/10.1002/tee.23896 doi: 10.1002/tee.23896
![]() |
[29] |
Wang ZQ, Gao SC, Zhou MC, et al. (2023) Information-theory-based nondominated sorting ant colony optimization for multiobjective feature selection in classification. IEEE Trans Cybern 53: 5276–5289. https://doi.org/10.1016/j.asoc.2023.110064 doi: 10.1016/j.asoc.2023.110064
![]() |
[30] |
Wang KY, Wang YR, Tao SC, et al. (2023) Spherical search algorithm with adaptive population control for global continuous optimization problems. Appl Soft Comput 132: 109845. https://doi.org/10.1016/j.asoc.2022.109845 doi: 10.1016/j.asoc.2022.109845
![]() |
[31] |
Yu Y, Gao SC, Zhou MC, et al. (2022) Scale-free network-based differential evolution to solve function optimization and parameter estimation of photovoltaic models. Swarm Evol Comput 74: 101142. https://doi.org/10.1016/j.swevo.2022.101142 doi: 10.1016/j.swevo.2022.101142
![]() |
[32] |
Xu Z, Gao SC, Yang HC, et al. (2021) SCJADE: Yet another state-of-the-art differential evolution algorithm. IEEJ Trans Electr Electron Eng 16: 644–646. https://doi.org/10.1002/tee.23340 doi: 10.1002/tee.23340
![]() |
[33] |
Gao SC, Wang KY, Tao SC, et al. (2021) A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models. Energy Convers Manage 230: 113784. https://doi.org/10.1016/j.enconman.2020.113784 doi: 10.1016/j.enconman.2020.113784
![]() |
[34] |
Qureshi TA, Warudkar V (2023) Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm. Environ Sci Pollut Res 30: 77436–77452. https://doi.org/10.1007/s11356-023-27849-7 doi: 10.1007/s11356-023-27849-7
![]() |
[35] |
Yang HC, Gao SC, Lei ZY, et al. (2023) An improved spherical evolution with enhanced exploration capabilities to address wind farm layout optimization problem. Eng Appl Artif Intell 123: 106198. https://doi.org/10.1016/j.engappai.2023.106198 doi: 10.1016/j.engappai.2023.106198
![]() |
[36] |
Yu Y, Zhang TF, Lei ZZ, et al. (2023) A chaotic local search-based LSHADE with enhanced memory storage mechanism for wind farm layout optimization. Appl Soft Comput 141: 110306. https://doi.org/10.1016/j.asoc.2023.110306 doi: 10.1016/j.asoc.2023.110306
![]() |
[37] |
Kunakote T, Sabangban N, Kumar S, et al. (2022) Comparative performance of twelve metaheuristics for wind farm layout optimisation. Arch Comput Methods Eng 29: 717–730. https://doi.org/10.1007/s11831-021-09586-7 doi: 10.1007/s11831-021-09586-7
![]() |
[38] |
Long H, Li PK, Gu W (2020) A data-driven evolutionary algorithm for wind farm layout optimization. Energy 208: 118310. https://doi.org/10.1016/j.energy.2020.118310 doi: 10.1016/j.energy.2020.118310
![]() |
[39] |
Gao SC, Zhou MC, Wang YR, et al. (2019) Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Trans Neural Networks Learn Syst 30: 601–604. https://doi.org/10.1109/TNNLS.2018.2846646 doi: 10.1109/TNNLS.2018.2846646
![]() |
[40] |
Gao SC, Zhou MC, Wang ZQ, et al. (2023) Fully complex-valued dendritic neuron model. IEEE Trans Neural Networks Learn Syst 34: 2105–2118. https://doi.org/10.1109/TNNLS.2021.3105901 doi: 10.1109/TNNLS.2021.3105901
![]() |
[41] |
Ju X, Liu F (2019) Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation. Appl Energy 248: 429–445. https://doi.org/10.1016/j.apenergy.2019.04.084 doi: 10.1016/j.apenergy.2019.04.084
![]() |
[42] |
Ju XL, Liu F, Wang L, et al. (2019) Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners. Energy Convers Manage 196: 1267–1281. https://doi.org/10.1016/j.enconman.2019.06.082 doi: 10.1016/j.enconman.2019.06.082
![]() |
[43] |
Zhang YY, Chen GY, Cheng L, et al. (2019) Methods to balance the exploration and exploitation in differential evolution from different scales: A survey. Neurocomputing 561: 126899. https://doi.org/10.1016/j.neucom.2023.126899 doi: 10.1016/j.neucom.2023.126899
![]() |
[44] |
Zhang ZH, Yu QR, Yang HC, et al. (2024) Triple-layered chaotic differential evolution algorithm for layout optimization of offshore wave energy converters. Expert Syst Appl 239: 122439. https://doi.org/10.1016/j.eswa.2023.122439 doi: 10.1016/j.eswa.2023.122439
![]() |
[45] |
Cai ZH, Yang X, Zhou MC, et al. (2023) Toward explicit control between exploration and exploitation in evolutionary algorithms: A case study of differential evolution. Inf Sci 649: 119656. https://doi.org/10.1016/j.ins.2023.119656 doi: 10.1016/j.ins.2023.119656
![]() |
[46] |
Gupta S, Singh S, Su R, et al. (2023) Multiple elite individual guided piecewise search-based differential evolution. IEEE/CAA J Autom Sin 10: 135–158. https://doi.org/10.1109/JAS.2023.123018 doi: 10.1109/JAS.2023.123018
![]() |
[47] |
Li XS, Li JY, Yang HC, et al. (2022) Population interaction network in representative differential evolution algorithms: Power-law outperforms Poisson distribution. Phys A 603: 127764. https://doi.org/10.1016/j.physa.2022.127764 doi: 10.1016/j.physa.2022.127764
![]() |
[48] |
Yu Y, Wang KY, Zhang TF, et al. (2022) A population diversity-controlled differential evolution for parameter estimation of solar photovoltaic models. Sustainable Energy Technol Assess 51: 101938. https://doi.org/10.1016/j.seta.2021.101938 doi: 10.1016/j.seta.2021.101938
![]() |
[49] | Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 1658–1665. https://doi.org/10.1109/CEC.2014.6900380 |
[50] | Mohamed AW, Hadi AA, Fattouh AM, et al. (2022) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, Spain, 145–152. https://doi.org/10.1109/CEC.2017.7969307 |
[51] |
Mosetti G, Poloni C, Diviacco B (1994) Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J Wind Eng Ind Aerodyn 51: 105–116. https://doi.org/10.1016/0167-6105(94)90080-9 doi: 10.1016/0167-6105(94)90080-9
![]() |
[52] |
Shakoor R, Hassan MY, Raheem A, et al. (2016) Wake effect modeling: A review of wind farm layout optimization using Jensen's model. Renewable Sustainable Energy Rev 58: 1048–1059. https://doi.org/10.1016/j.rser.2015.12.229 doi: 10.1016/j.rser.2015.12.229
![]() |
[53] |
Rao RV, Keesari HS (2018) Multi-team perturbation guiding jaya algorithm for optimization of wind farm layout. Appl Soft Comput 71: 800–815. https://doi.org/10.1016/j.asoc.2018.07.036 doi: 10.1016/j.asoc.2018.07.036
![]() |
[54] |
Sorkhabi SYD, Romero DA, Beck JC, et al. (2018) Constrained multi-objective wind farm layout optimization: Novel constraint handling approach based on constraint programming. Renewable Energy 126: 341–353. https://doi.org/10.1016/j.renene.2018.03.053 doi: 10.1016/j.renene.2018.03.053
![]() |
[55] |
Zergane S, Smaili A, Masson C (2018) Optimization of wind turbine placement in a wind farm using a new pseudo-random number generation method. Renewable Energy 125: 166–171. https://doi.org/10.1016/j.renene.2018.02.082 doi: 10.1016/j.renene.2018.02.082
![]() |
[56] |
Rizk-Allah RM, Hassanien AE (2023) A hybrid equilibrium algorithm and pattern search technique for wind farm layout optimization problem. ISA Trans 132: 402–418. https://doi.org/10.1016/j.isatra.2022.06.014 doi: 10.1016/j.isatra.2022.06.014
![]() |
[57] |
Sun HY, Yang HX (2023) Wind farm layout and hub height optimization with a novel wake model. Appl Energy 348: 121554. https://doi.org/10.1016/j.apenergy.2023.121554 doi: 10.1016/j.apenergy.2023.121554
![]() |
[58] |
González JS, Rodriguez AGG, Mora JC, et al. (2010) Optimization of wind farm turbines layout using an evolutive algorithm. Renewable Energy 35: 1671–1681. https://doi.org/10.1016/j.renene.2010.01.010 doi: 10.1016/j.renene.2010.01.010
![]() |
[59] |
Abdelsalam AM, El-Shorbagy MA (2018) Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renewable Energy 123: 748–755. https://doi.org/10.1016/j.renene.2018.02.083 doi: 10.1016/j.renene.2018.02.083
![]() |
[60] |
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1: 67–82. https://doi.org/10.1109/4235.585893 doi: 10.1109/4235.585893
![]() |
[61] |
Sui QY, Yu Y, Wang KY, et al. (2024) Best-worst individuals driven multiple-layered differential evolution. Inf Sci 655: 119889. https://doi.org/10.1016/j.ins.2023.119889 doi: 10.1016/j.ins.2023.119889
![]() |
[62] | Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, 71–78. https://doi.org/10.1109/CEC.2013.6557555 |
[63] |
Gao SC, Yu Y, Wang YR (2021) Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans Syst Man Cybern: Syst 51: 3954–3967. https://doi.org/10.1109/TSMC.2019.2956121 doi: 10.1109/TSMC.2019.2956121
![]() |
[64] | Hansen N (2006) Advances on estimation of distribution algorithms. In: Jose A. Lozano, Pedro Larrañaga, Iñaki Inza, Endika Bengoetxea, Towards a New Evolutionary Computation, 1st Ed, Springer Berlin, Heidelberg. 192: 75–102. https://doi.org/10.1007/3-540-32494-1_4 |