The grey wolf optimization algorithm (GWO) is a new metaheuristic algorithm. The GWO has the advantages of simple structure, few parameters to adjust, and high efficiency, and has been applied in various optimization problems. However, the orginal GWO search process is guided entirely by the best three wolves, resulting in low population diversity, susceptibility to local optima, slow convergence rate, and imbalance in development and exploration. In order to address these shortcomings, this paper proposes an adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO). First, the convergence factor was segmented and nonlinearized to balance the global search and local search of the algorithm and improve the convergence rate. Second, the wolves in the original GWO approach the leader in a straight line, which is too simple and ignores a lot of information on the path. Therefore, a dynamic logarithmic spiral that nonlinearly decreases with the number of iterations was introduced to expand the search range of the algorithm in the early stage and enhance local development in the later stage. Then, the fixed step size in the original GWO can lead to algorithm oscillations and an inability to escape local optima. A dynamic self-learning step size was designed to help the algorithm escape from local optima and prevent oscillations by reasonably learning the current evolution success rate and iteration count. Finally, the original GWO has low population diversity, which makes the algorithm highly susceptible to becoming trapped in local optima. A novel position update strategy was proposed, using the global optimum and randomly generated positions as learning samples, and dynamically controlling the influence of learning samples to increase population diversity and avoid premature convergence of the algorithm. Through comparison with traditional algorithms, such as GWO, PSO, WOA, and the new variant algorithms EOGWO and SOGWO on 23 classical test functions, ASGWO can effectively improve the convergence accuracy and convergence speed, and has a strong ability to escape from local optima. In addition, ASGWO also has good performance in engineering problems (gear train problem, ressure vessel problem, car crashworthiness problem) and feature selection.
Citation: Yijie Zhang, Yuhang Cai. Adaptive dynamic self-learning grey wolf optimization algorithm for solving global optimization problems and engineering problems[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 3910-3943. doi: 10.3934/mbe.2024174
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The grey wolf optimization algorithm (GWO) is a new metaheuristic algorithm. The GWO has the advantages of simple structure, few parameters to adjust, and high efficiency, and has been applied in various optimization problems. However, the orginal GWO search process is guided entirely by the best three wolves, resulting in low population diversity, susceptibility to local optima, slow convergence rate, and imbalance in development and exploration. In order to address these shortcomings, this paper proposes an adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO). First, the convergence factor was segmented and nonlinearized to balance the global search and local search of the algorithm and improve the convergence rate. Second, the wolves in the original GWO approach the leader in a straight line, which is too simple and ignores a lot of information on the path. Therefore, a dynamic logarithmic spiral that nonlinearly decreases with the number of iterations was introduced to expand the search range of the algorithm in the early stage and enhance local development in the later stage. Then, the fixed step size in the original GWO can lead to algorithm oscillations and an inability to escape local optima. A dynamic self-learning step size was designed to help the algorithm escape from local optima and prevent oscillations by reasonably learning the current evolution success rate and iteration count. Finally, the original GWO has low population diversity, which makes the algorithm highly susceptible to becoming trapped in local optima. A novel position update strategy was proposed, using the global optimum and randomly generated positions as learning samples, and dynamically controlling the influence of learning samples to increase population diversity and avoid premature convergence of the algorithm. Through comparison with traditional algorithms, such as GWO, PSO, WOA, and the new variant algorithms EOGWO and SOGWO on 23 classical test functions, ASGWO can effectively improve the convergence accuracy and convergence speed, and has a strong ability to escape from local optima. In addition, ASGWO also has good performance in engineering problems (gear train problem, ressure vessel problem, car crashworthiness problem) and feature selection.
Topological indices have become an important research topic associated with the study of their mathematical and computational properties and, fundamentally, for their multiple applications to various areas of knowledge (see, e.g., [1,2,3]). Within the study of mathematical properties, we will contribute to the study of inequalities and optimization problems associated with topological indices. Our main goals are the Sombor indices, introduced by Gutman in [4].
In what follows, G=(V(G),E(G)) will be a finite undirected graph, and we will assume that each vertex has at least a neighbor. We denote by dw the degree of the vertex w, i.e., the number of neighbors of w. We denote by uv the edge joining the vertices u and v (or v and u). For each graph G, its Sombor index is
SO(G)=∑uv∈E(G)√d2u+d2v. |
In the same paper is also defined the reduced Sombor index by
SOred(G)=∑uv∈E(G)√(du−1)2+(dv−1)2. |
In [5] it is shown that these indices have a good predictive potential.
Also, the modified Sombor index of G was proposed in [6] as
mSO(G)=∑uv∈E(G)1√d2u+d2v. | (1.1) |
In addition, two other Sombor indices have been introduced: the first Banhatti-Sombor index [7]
BSO(G)=∑uv∈E(G)√1d2u+1d2v | (1.2) |
and the α-Sombor index [8]
SOα(G)=∑uv∈E(G)(dαu+dαv)1/α, | (1.3) |
here α∈R∖{0}. In fact, there is a general index that includes most Sombor indices listed above: the first (α,β)–KA index of G which was introduced in [9] as
KAα,β(G)=KA1α,β(G)=∑uv∈E(G)(dαu+dαv)β, | (1.4) |
with α,β∈R. Note that SO(G)=KA2,1/2(G), mSO(G)=KA2,−1/2(G), BSO(G)=KA−2,1/2(G), and SOα(G)=KAα,1/α(G). Also, we note that KA1,β(G) equals the general sum-connectivity index [10] χβ(G)=∑uv∈E(G)(du+dv)β. Reduced versions of SO(G), mSO(G) and KAα,β(G) were also introduced in [4,6,11], e.g., the reduced (α,β)–KA index is
redKAα,β(G)=∑uv∈E(G)((du−1)α+(dv−1)α)β. |
If α<0, then redKAα,β(G) is just defined for graphs without pendant vertices (recall that a vertex is said pendant if its degree is equal to 1).
Since I. Gutman initiated the study of the mathematical properties of Sombor index in [4], many papers have continued this study, see e.g., [12,13,14,15,16,17,18].
Our main aim is to obtain new bounds of Sombor indices, and to characterize the graphs where equality occurs. In particular, we have obtained bounds for Sombor indices relating them with the first Zagreb index, the forgotten index and the first variable Zagreb index. Also, we solve some extremal problems for Sombor indices.
The following inequalities are known for x,y>0:
xa+ya<(x+y)a≤2a−1(xa+ya)if a>1,2a−1(xa+ya)≤(x+y)a<xa+yaif 0<a<1,(x+y)a≤2a−1(xa+ya)if a<0, |
and the second, third or fifth equality is attained for each a if and only if x=y. These inequalities allow to obtain the following result relating KA indices.
Theorem 1. Let G be any graph and α,β,λ∈R∖{0}.Then
KAαβ/λ,λ(G)<KAα,β(G)≤2β−λKAαβ/λ,λ(G)if β>λ,βλ>0,2β−λKAαβ/λ,λ(G)≤KAα,β(G)<KAαβ/λ,λ(G)if β<λ,βλ>0,KAα,β(G)≤2β−λKAαβ/λ,λ(G)if β<0,λ>0,KAα,β(G)≥2β−λKAαβ/λ,λ(G)if β>0,λ<0, |
and the second, third, fifth or sixth equality is attained for each α,β,λ if and only if all the connected components of G are regular graphs.
Proof. If a=β/λ, x=dαu and y=dαv, then the previous inequalities give
dαβ/λu+dαβ/λv<(dαu+dαv)β/λ≤2β/λ−1(dαβ/λu+dαβ/λv)if β/λ>1,2β/λ−1(dαβ/λu+dαβ/λv)≤(dαu+dαv)β/λ<dαβ/λu+dαβ/λvif 0<β/λ<1,(dαu+dαv)β/λ≤2β/λ−1(dαβ/λu+dαβ/λv)if β/λ<0, |
and the second, third or fifth equality is attained if and only if du=dv.
Hence, we obtain
(dαβ/λu+dαβ/λv)λ<(dαu+dαv)β≤2β−λ(dαβ/λu+dαβ/λv)λif β/λ>1,λ>0,2β−λ(dαβ/λu+dαβ/λv)λ≤(dαu+dαv)β<(dαβ/λu+dαβ/λv)λif β/λ>1,λ<0,2β−λ(dαβ/λu+dαβ/λv)λ≤(dαu+dαv)β<(dαβ/λu+dαβ/λv)λif 0<β/λ<1,λ>0,(dαβ/λu+dαβ/λv)λ<(dαu+dαv)β≤2β−λ(dαβ/λu+dαβ/λv)λif 0<β/λ<1,λ<0,(dαu+dαv)β≤2β−λ(dαβ/λu+dαβ/λv)λif β<0,λ>0,(dαu+dαv)β≥2β−λ(dαβ/λu+dαβ/λv)λif β>0,λ<0, |
and the equality in the non-strict inequalities is tight if and only if du=dv.
If we sum on uv∈E(G) these inequalities, then we obtain (1).
Remark 2. Note that the excluded case β=λ in Theorem 1 is not interesting, since KAαβ/λ,λ(G)=KAα,β(G) if β=λ.
The argument in the proof of Theorem 1 also allows to obtain the following result relating reduced KA indices.
Theorem 3. Let G be any graph and α,β,λ∈R∖{0}.If α<0 or αβλ<0, we also assume that G does not have pendant vertices.Then
redKAαβ/λ,λ(G)<redKAα,β(G)≤2β−λredKAαβ/λ,λ(G)if β>λ,βλ>0,2β−λredKAαβ/λ,λ(G)≤redKAα,β(G)<redKAαβ/λ,λ(G)if β<λ,βλ>0,redKAα,β(G)≤2β−λredKAαβ/λ,λ(G)if β<0,λ>0,redKAα,β(G)≥2β−λredKAαβ/λ,λ(G)if β>0,λ<0, |
and the second, third, fifth or sixth equality is attained for each α,β,λ if and only if all the connected components of G are regular graphs.
If we take β=1/α and μ=1/λ in Theorem 1, we obtain the following inequalities for the α-Sombor index.
Corollary 4. Let G be any graph and α,μ∈R∖{0}.Then
SOμ(G)<SOα(G)≤21/α−1/μSOμ(G)if μ>α,αμ>0,21/α−1/μSOμ(G)≤SOα(G)<SOμ(G)if μ<α,αμ>0,SOα(G)≤21/α−1/μSOμ(G)if α<0,μ>0, |
and the second, third or fifth equality is attained for each α,μ if and only if all the connected components of G are regular graphs.
Recall that one of the most studied topological indices is the first Zagreb index, defined by
M1(G)=∑u∈V(G)d2u. |
If we take μ=1 in Corollary 4, we obtain the following result.
Corollary 5. Let G be any graph and α∈R∖{0}.Then
M1(G)<SOα(G)≤21/α−1M1(G)if 0<α<1,21/α−1M1(G)≤SOα(G)<M1(G)if α>1,SOα(G)≤21/α−1M1(G)if α<0, |
and the second, third or fifth equality is attained for each α if and only if all the connected components of G are regular graphs.
If we take α=2, β=−1/2 and λ=1/2 in Theorem 1, we obtain the following inequality relating the modified Sombor and the first Banhatti-Sombor indices.
Corollary 6. Let G be any graph.Then
mSO(G)≤12BSO(G) |
and the bound is tight if and only if all the connected components of G are regular graphs
In [19,20,21], the first variable Zagreb index is defined by
Mα1(G)=∑u∈V(G)dαu, |
with α∈R.
Note that Mα1 generalizes numerous degree–based topological indices which earlier have independently been studied. For α=2, α=3, α=−1/2, and α=−1, Mα1 is, respectively, the ordinary first Zagreb index M1, the forgotten index F, the zeroth–order Randić index 0R, and the inverse index ID [2,22].
The next result relates the KAα,β and Mα+11 indices.
Theorem 7. Let G be any graph with maximum degree Δ, minimum degree δ and m edges, and α∈R∖{0}, β>0.Then
KAα,β(G)≥(Mα+11(G)+2Δα/2δα/2m√2(Δα/2+δα/2))2βif 0<β<1/2,KAα,β(G)≥(Mα+11(G)+2Δα/2δα/2m√2(Δα/2+δα/2))2βm1−2βif β≥1/2, |
and the second equality is attained for some α,β if and only if G is a regular graph.
Proof. If uv∈E(G) and α>0, then
√2δα/2≤√dαu+dαv≤√2Δα/2. |
If α<0, then the converse inequalities hold. Hence,
(√dαu+dαv−√2δα/2)(√2Δα/2−√dαu+dαv)≥0,√2(Δα/2+δα/2)√dαu+dαv≥dαu+dαv+2Δα/2δα/2. |
Since
∑uv∈E(G)(dαu+dαv)=∑u∈V(G)dudαu=∑u∈V(G)dα+1u=Mα+11(G), |
If 0<β<1/2, then 1/(2β)>1 and
∑uv∈E(G)√dαu+dαv=∑uv∈E(G)((dαu+dαv)β)1/(2β)≤(∑uv∈E(G)(dαu+dαv)β)1/(2β)=KAα,β(G)1/(2β). |
Consequently, we obtain
KAα,β(G)1/(2β)≥Mα+11(G)+2Δα/2δα/2m√2(Δα/2+δα/2). |
If β≥1/2, then 2β≥1 and Hölder inequality gives
∑uv∈E(G)√dαu+dαv=∑uv∈E(G)((dαu+dαv)β)1/(2β)≤(∑uv∈E(G)(dαu+dαv)β)1/(2β)(∑uv∈E(G)12β/(2β−1))(2β−1)/(2β)=m(2β−1)/(2β)KAα,β(G)1/(2β). |
Consequently, we obtain
KAα,β(G)1/(2β)≥Mα+11(G)+2Δα/2δα/2m√2(Δα/2+δα/2)m(1−2β)/(2β). |
If G is regular, then
(Mα+11(G)+2Δα/2δα/2m√2(Δα/2+δα/2))2βm1−2β=(2Δαm+2Δαm√22Δα/2)2βm1−2β=(√2Δα/2m)2βm1−2β=(2Δα)βm=KAα,β(G). |
If the second equality is attained for some α,β, then we have dαu+dαv=2δα or dαu+dαv=2Δα for each uv∈E(G). Also, the equality in Hölder inequality gives that there exists a constant c such that dαu+dαv=c for every uv∈E(G). Hence, we have either dαu+dαv=2δα for each edge uv or dαu+dαv=2Δα for each edge uv, and hence, G is regular.
If we take α=2 and β=1/2 in Theorem 7 we obtain:
Corollary 8. Let G be any graph with maximum degree Δ and minimum degree δ, and m edges.Then
SO(G)≥F(G)+2Δδm√2(Δ+δ), |
and the bound is tight if and only if G is regular.
In order to prove Theorem 10 below we need an additional technical result. A converse of Hölder inequality appears in [23,Theorem 3], which, in the discrete case, can be stated as follows [23,Corollay 2].
Proposition 9. Consider constants 0<α≤β and 1<p,q<∞ with 1/p+1/q=1.If wk,zk≥0 satisfy αzqk≤wpk≤βzqk for 1≤k≤n, then
(n∑k=1wpk)1/p(n∑k=1zqk)1/q≤Cp(α,β)n∑k=1wkzk, |
where
Cp(α,β)={1p(αβ)1/(2q)+1q(βα)1/(2p),when 1<p<2,1p(βα)1/(2q)+1q(αβ)1/(2p),when p≥2. |
If (w1,…,wn)≠0, then the bound is tight if and only if wpk=αzqkfor each 1≤k≤n and α=β.
Recall that a bipartite graph with X and Y partitions is called (a,b)-biregular if all vertices of X have degree a and all vertices of Y have degree b.
The next result relates several KA indices.
Theorem 10. Let G be any graph, α,β,μ∈R and p>1.Then
DppKAα,p(β−μ)(G)KAα,pμ/(p−1)(G)p−1≤KAα,β(G)p≤KAα,p(β−μ)(G)KAα,pμ/(p−1)(G)p−1 |
where
Dp={Cp((2δα)p(β−μpp−1),(2Δα)p(β−μpp−1))−1,if α(β−μpp−1)≥0,Cp((2Δα)p(β−μpp−1),(2δα)p(β−μpp−1))−1,if α(β−μpp−1)<0, |
and Cp is the constant in Proposition 9. The equality in the upper(lower) bound is tight for each α,β,μ,p if G is a biregular graph (with α(β−μpp−1)≠0 if and only if G is a regular graph.)
Proof. Hölder inequality gives
KAα,β(G)=∑uv∈E(G)(dαu+dαv)β−μ(dαu+dαv)μ≤(∑uv∈E(G)(dαu+dαv)p(β−μ))1/p(∑uv∈E(G)(dαu+dαv)pμ/(p−1))(p−1)/p,KAα,β(G)p≤KAα,p(β−μ)(G)KAα,pμ/(p−1)(G)p−1. |
If G is a biregular graph with m edges, we obtain
KAα,p(β−μ)(G)KAα,pμ/(p−1)(G)p−1=(Δα+δα)p(β−μ)m((Δα+δα)pμ/(p−1)m)p−1=(Δα+δα)p(β−μ)(Δα+δα)pμmp=((Δα+δα)βm)p=KAα,β(G)p. |
Since
(dαu+dαv)p(β−μ)(dαu+dαv)pμ/(p−1)=(dαu+dαv)p(β−μpp−1), |
if αp(β−μpp−1)≥0, then
(2δα)p(β−μpp−1)≤(dαu+dαv)p(β−μ)(dαu+dαv)pμ/(p−1)≤(2Δα)p(β−μpp−1), |
and if αp(β−μpp−1)<0, then
(2Δα)p(β−μpp−1)≤(dαu+dαv)p(β−μ)(dαu+dαv)pμ/(p−1)≤(2δα)p(β−μpp−1). |
Proposition 9 gives
KAα,β(G)=∑uv∈E(G)(dαu+dαv)β−μ(dαu+dαv)μ≥Dp(∑uv∈E(G)(dαu+dαv)p(β−μ))1/p(∑uv∈E(G)(dαu+dαv)pμ/(p−1))(p−1)/p,KAα,β(G)p≥DppKAα,p(β−μ)(G)KAα,pμ/(p−1)(G)p−1. |
Proposition 9 gives that the equality is tight in this last bound for some α,β,μ,p with α(β−μpp−1)≠0 if and only if
(2δα)p(β−μpp−1)=(2Δα)p(β−μpp−1)⇔δ=Δ, |
i.e., G is regular.
If we take β=0 in Theorem 10 we obtain the following result.
Corollary 11. Let G be any graph with m edges, α,μ∈R and p>1.Then
KAα,−pμ(G)KAα,pμ/(p−1)(G)p−1≥mp. |
The equality in the bound is tight for each α,μ,p if G is a biregular graph.
If we take α=2, β=0, p=2 and μ=1/4 in Theorem 10 we obtain the following result.
Corollary 12. Let G be any graph with maximum degree Δ, minimum degree δ and m edges, then
m2≤mSO(G)SO(G)≤(Δ+δ)24Δδm2. |
The equality in the upper bound is tight if and only if G is regular.The equality in the lower bound is tight if G is a biregular graph.
Note that the following result improves the upper bound in Corollary 5 when α>1.
Theorem 13. Let G be any graph with minimum degree δ, and α≥1.Then
21/α−1M1(G)≤SOα(G)≤M1(G)−(2−21/α)δ, |
and the equality holds for some α>1 in each bound if and only if G is regular.
Proof. The lower bound follows from Corollary 5. Let us prove the upper bound.
First of all, we are going to prove that
(xα+yα)1/α≤x+(21/α−1)y | (2.1) |
for every α≥1 and x≥y≥0. Since (2.1) is direct for α=1, it suffices to consider the case α>1.
We want to compute the minimum value of the function
f(x,y)=x+(21/α−1)y |
with the restrictions g(x,y)=xα+yα=1, x≥y≥0. If (x,y) is a critical point, then there exists λ∈R such that
1=λαxα−1,21/α−1=λαyα−1, |
and so, (y/x)α−1=21/α−1 and y=(21/α−1)1/(α−1)x; this fact and the equality xα+yα=1 imply
(1+(21/α−1)α/(α−1))xα=1,x=(1+(21/α−1)α/(α−1))−1/α,y=(21/α−1)1/(α−1)(1+(21/α−1)α/(α−1))−1/α,f(x,y)=(1+(21/α−1)α/(α−1))−1/α+(21/α−1)(21/α−1)1/(α−1)(1+(21/α−1)α/(α−1))−1/α=(1+(21/α−1)α/(α−1))−1/α+(21/α−1)α/(α−1)(1+(21/α−1)α/(α−1))−1/α=(1+(21/α−1)α/(α−1))(α−1)/α>1. |
If y=0, then x=1 and f(x,y)=1.
If y=x, then x=2−1/α=y and
f(x,y)=2−1/α+(21/α−1)2−1/α=1. |
Hence, f(x,y)≥1 and the bound is tight if and only if y=0 or y=x. By homogeneity, we have f(x,y)≥1 for every x≥y≥0 and the bound is tight if and only if y=0 or y=x. This finishes the proof of (2.1).
Consequently,
(dαu+dαv)1/α≤du+(21/α−1)dv=du+dv−(2−21/α)dv |
for each α≥1 and du≥dv. Thus,
(dαu+dαv)1/α≤du+dv−(2−21/α)δ |
for each α≥1 and uv∈E(G), and the equality holds for some α>1 if and only if du=dv=δ. Therefore,
SOα(G)≤M1(G)−(2−21/α)δ, |
and the equality holds for some α>1 if and only if du=dv=δ for every uv∈E(G), i.e., G is regular.
Corollary 14. Let G be any graph with minimum degree δ.Then
2−1/2M1(G)≤SO(G)≤M1(G)−(2−√2)δ, |
and the equality holds in each bound if and only if G is regular.
The upper bound in Corollary 14 appears in [14,Theorem 7]. Hence, Theorem 13 generalizes [14,Theorem 7].
A family of topological indices, named Adriatic indices, was put forward in [24,25]. Twenty of them were selected as significant predictors in Mathematical Chemistry. One of them, the inverse sum indeg index, ISI, was singled out in [25] as a significant predictor of total surface area of octane isomers. This index is defined as
ISI(G)=∑uv∈E(G)dudvdu+dv=∑uv∈E(G)11du+1dv. |
In the last years there has been an increasing interest in the mathematical properties of this index. We finish this section with two inequalities relating the Sombor, the first Zagreb and the inverse sum indeg indices.
Theorem 15. Let G be any graph, then
√2(M1(G)−2ISI(G))≥SO(G)>M1(G)−2ISI(G) |
and the upper bound is tight if and only if all the connected components of G are regular graphs.
Proof. It is well-known that for x,y>0, we have
x2+y2<(x+y)2≤2(x2+y2),√x2+y2<x+y≤√2√x2+y2, |
and the equality
√d2u+d2v√d2u+d2v+2dudv=(du+dv)2 |
give
(du+dv)√d2u+d2v+2dudv>(du+dv)2,√d2u+d2v+2dudvdu+dv>du+dv,SO(G)+2ISI(G)>M1(G). |
In a similar way, we obtain
1√2(du+dv)√d2u+d2v+2dudv≤(du+dv)2,√d2u+d2v+√22dudvdu+dv≤√2(du+dv),SO(G)+2√2ISI(G)≤√2M1(G). |
The equality in this last inequality is tight if and only if 2(d2u+d2v)=(du+dv)2 for each edge uv, i.e., du=dv for every uv∈E(G), and this happens if and only if all the connected components of G are regular graphs.
We start this section with a technical result.
Proposition 16. Let G be any graph, u,v∈V(G) with uv∉E(G), and α,β∈R∖{0} with αβ>0.Then KAα,β(G∪{uv})>KAα,β(G).If α>0, then redKAα,β(G∪{uv})>redKAα,β(G).Furthermore, if α<0 and G does not have pendant vertices, then redKAα,β(G∪{uv})>redKAα,β(G).
Proof. Let {w1,…,wdu} and {w1,…,wdv} be the sets of neighbors of u and v in G, respectively. Since αβ>0, the function
U(x,y)=(xα+yα)β |
is strictly increasing in each variable if x,y>0. Hence,
KAα,β(G∪{uv})−KAα,β(G)=((du+1)α+(dv+1)α)β++du∑j=1(((du+1)α+dαwj)β−(dαu+dαwj)β)+dv∑k=1(((dv+1)α+dαwk)β−(dαv+dαwk)β)>((du+1)α+(dv+1)α)β>0. |
The same argument gives the results for the redKAα,β index.
Given an integer number n≥2, let Γ(n) (respectively, Γc(n)) be the set of graphs (respectively, connected graphs) with n vertices.
We study in this section the extremal graphs for the KAα,β index on Γc(n) and Γ(n).
Theorem 17. Consider α,β∈R∖{0} with αβ>0, and an integer n≥2.
(1) The complete graph Kn is the unique graph that maximizes KAα,β on Γc(n) or Γ(n).
(2) Any graph that minimizes KAα,β on Γc(n) is a path.
(3) If n is even, then the union of n/2 paths P2 is the unique graph that minimizes KAα,β on Γ(n).If n is odd, then the union of (n−3)/2 paths P2 with a path P3 isthe unique graph that minimizes KAα,β on Γ(n).
(4) Furthermore, if α,β>0, then the three previous statements hold if we replace KAα,β with redKAα,β.
Proof. Let G be a graph with order n, minimum degree δ and m edges.
Items (1) and (2) follow directly from Proposition 16.
(3) Assume that n is even. It is well known that the sum of the degrees of a graph is equal to twice the number of edges of the graph (handshaking lemma). Thus, 2m≥nδ≥n. Since αβ>0, the function
U(x,y)=(xα+yα)β |
is strictly increasing in each variable if x,y>0. Hence, for any graph G∈Γ(n), we have
KAα,β(G)=∑uv∈E(G)(dαu+dαv)β≥∑uv∈E(G)(1α+1α)β=2βm≥2βn2=2β−1n, |
and the equality is tight in the inequality if and only if du=1 for all u∈V(G), i.e., G is the union of n/2 path graphs P2.
Finally, assume that n is odd. Fix a graph G∈Γ(n). If du=1 for every u∈V(G), then handshaking lemma gives 2m=n, a contradiction (recall that n is odd). Therefore, there exists a vertex w with dw≥2. By handshaking lemma we have 2m≥(n−1)δ+2≥n+1. Recall that the set of neighbors of the vertex w is denoted by N(w). Since U(x,y) is a strictly increasing function in each variable, we obtain
KAα,β(G)=∑u∈N(w)(dαu+dαw)β+∑uv∈E(G),u,v≠w(dαu+dαv)β≥∑u∈N(w)(1α+2α)β+∑uv∈E(G),u,v≠w(1α+1α)β≥2(1+2α)β+2β(m−2)≥2(1+2α)β+2β(n+12−2)=2(1+2α)β+2βn−32, |
and the bound is tight if and only if du=1 for all u∈V(G)∖{w}, and dw=2. Hence, G is the union of (n−3)/2 path graphs P2 and a path graph P3.
(4) If α,β>0, then the same argument gives the results for the redKAα,β index.
We deal now with the optimization problem for redKAα,β when α,β<0.
Given an integer number n≥3, we denote by Γwp(n) (respectively, Γwpc(n)) the set of graphs (respectively, connected graphs) with n vertices and without pendant vertices.
Theorem 18. Consider α,β<0, and an integer n≥3.
(1) The cycle graph Cn is the unique graph that minimizes redKAα,β on Γwpc(n).
(2) The union of cycle graphs are the only graphs that minimize redKAα,β on Γwp(n).
(3) The complete graph Kn is the unique graph that maximizes redKAα,β on Γwpc(n) or Γwp(n).
Proof. Let G be a graph with order n, minimum degree δ and m edges. Since a graph without pendant vertices satisfies δ≥2, handshaking lemma gives 2m≥nδ≥2n. Since α,β<0, the function
U(x,y)=(xα+yα)β |
is strictly increasing in each variable if x,y>0. Hence, for any graph G∈Γwp(n), we have
KAα,β(G)=∑uv∈E(G)(dαu+dαv)β≥∑uv∈E(G)(2α+2α)β=2(α+1)βm≥2(α+1)βn, |
and the inequality is tight if and only if du=2 for all u∈V(G), i.e., the graph G is the union of cycle graphs. If G is connected, then it is the cycle graph Cn.
Item (3) follows from Proposition 16.
In this paper, we contributed to the study of inequalities and optimization problems associated with topological indices. In particular, we obtained new lower and upper optimal bounds of general Sombor indices, and we characterized the graphs where equality occurs.
Specifically, we have obtained inequalities for these indices relating them with other indices: the first Zagreb index, the forgotten index and the first variable Zagreb index. Finally, we solve some extremal problems for general Sombor indices
We would like to thank the reviewers by their careful reading of the manuscript and their suggestions which have improved the presentation of this work. The research of José M. Rodríguez and José M. Sigarreta was supported by a grant from Agencia Estatal de Investigación (PID2019-106433GB- ´ I00/AEI/10.13039/501100011033), Spain. The research of Jose M. Rodríguez is supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M23), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).
All authors declare no conflicts of interest in this paper.
[1] | F. Jiang, L. Wang, L. Bai, An adaptive evolutionary whale optimization algorithm, in 2021 33rd Chinese Control and Decision Conference (CCDC), (2021), 4610–4614. https://doi.org/10.1109/CCDC52312.2021.9601898 |
[2] | J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95-international conference on neural networks, 4 (1995), 1942–1948. https://doi.org/10.1109/ICNN.1995.488968 |
[3] |
S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
![]() |
[4] |
S. Mirjalili, The ant lion optimizer, Adv. Eng. Software, 83 (2015), 80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010 doi: 10.1016/j.advengsoft.2015.01.010
![]() |
[5] |
S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software, 69 (2014), 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
![]() |
[6] |
G. M. Komaki, V. Kayvanfar, Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time, J. Comput. Sci., 8 (2015), 109–120. https://doi.org/10.1016/j.jocs.2015.03.011 doi: 10.1016/j.jocs.2015.03.011
![]() |
[7] |
J. Liu, J. Yang, H. Liu, X. Tian, M. Gao, An improved ant colony algorithm for robot path planning, Soft Comput., 21 (2017), 5829–5839. https://doi.org/10.1007/s00500-016-2161-7 doi: 10.1007/s00500-016-2161-7
![]() |
[8] |
M. H. Sulaiman, Z. Mustaffa, M. R. Mohamed, O. Aliman, Using the gray wolf optimizer for solving optimal reactive power dispatch problem, Appl. Soft Comput., 32 (2015), 286–292. https://doi.org/10.1016/j.asoc.2015.03.041 doi: 10.1016/j.asoc.2015.03.041
![]() |
[9] |
R. E. Precup, R. C. David, E. M. Petriu, Grey wolf optimizer algorithm-based tuning of fuzzy control systems with reduced parametric sensitivity, IEEE Trans. Ind. Electron., 64 (2016), 527–534. https://doi.org/10.1109/tie.2016.2607698 doi: 10.1109/tie.2016.2607698
![]() |
[10] |
A. K. M. Khairuzzaman, S. Chaudhury, Multilevel thresholding using grey wolf optimizer for image segmentation, Expert Syst. Appl., 86 (2017), 64–76. https://doi.org/10.1016/j.eswa.2017.04.029 doi: 10.1016/j.eswa.2017.04.029
![]() |
[11] | R. E. Precup, R. C. David, R. C. Roman, A. I. Szedlak-Stinean, E. M. Petriu, Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm Int. J. Syst. Sci., 54 (2023), 2941–2956. https://doi.org/10.1080/00207721.2021.1927236 |
[12] |
S. Saremi, S. Z. Mirjalili, S. M. Mirjalili, Evolutionary population dynamics and grey wolf optimizer, Neural Comput. Appl., 26 (2015), 1257–1263. https://doi.org/10.1007/s00521-014-1806-7 doi: 10.1007/s00521-014-1806-7
![]() |
[13] |
C. A. Bojan-Dragos, R. E. Precup, S. Preitl, R. C. Roman, E. L. Hedrea, A. I. Szedlak-Stinean, GWO-based optimal tuning of type-1 and type-2 fuzzy controllers for electromagnetic actuated clutch systems, IFAC-PapersOnLine, 54 (2021), 189–194. https://doi.org/10.1016/j.ifacol.2021.10.032 doi: 10.1016/j.ifacol.2021.10.032
![]() |
[14] |
S. Wang, Y. Fan, S. Jin, P. Takyi-Aninakwa, C. Fernandez, Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries, Reliab. Eng. Syst. Saf., 230 (2023), 108920. https://doi.org/10.1016/j.ress.2022.108920 doi: 10.1016/j.ress.2022.108920
![]() |
[15] |
S. Wang, F. Wu, P. Takyi-Aninakwa, C. Fernandez, D. I. Stroe, Q. Huang, Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations, Energy, 284 (2023), 128677. https://doi.org/10.1016/j.energy.2023.128677 doi: 10.1016/j.energy.2023.128677
![]() |
[16] | S. Gottam, S. J. Nanda, R. K. Maddila, A CNN-LSTM model trained with grey wolf optimizer for prediction of household power consumption, in 2021 IEEE International Symposium on Smart Electronic Systems (iSES), (2021), 355–360. https://doi.org/10.1109/iSES52644.2021.00089 |
[17] |
W. Long, J. Jiao, X. Liang, M. Tang, An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization, Eng. Appl. Artif. Intell., 68 (2018), 63–80. https://doi.org/10.1016/j.engappai.2017.10.024 doi: 10.1016/j.engappai.2017.10.024
![]() |
[18] |
Z. J. Teng, J. L. Lv, L. W. Guo, An improved hybrid grey wolf optimization algorithm, Soft Comput., 23 (2019), 6617–6631. https://doi.org/10.1007/s00500-018-3310-y doi: 10.1007/s00500-018-3310-y
![]() |
[19] | A. Kishor, P. K. Singh, Empirical study of grey wolf optimizer, in Proceedings of Fifth International Conference on Soft Computing for Problem solving, (2016), 1037–1049. |
[20] |
M. Pradhan, P. K. Roy, T. Pal, Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system, Ain Shams Eng. J., 9 (2018), 2015–2025. https://doi.org/10.1016/j.asej.2016.08.023 doi: 10.1016/j.asej.2016.08.023
![]() |
[21] |
L. Rodriguez, O. Castillo, J. Soria, P. Melin, F. Valdez, C. I. Gonzalez, A fuzzy hierarchical operator in the grey wolf optimizer algorithm, Appl. Soft Comput., 57 (2017), 315–328. https://doi.org/10.1016/j.asoc.2017.03.048 doi: 10.1016/j.asoc.2017.03.048
![]() |
[22] |
J. Xu, F. Yan, O. G. Ala, L. Su, F. Li, Chaotic dynamic weight grey wolf optimizer for numerical function optimization, J. Intell. Fuzzy Syst., 37 (2019), 2367–2384. https://doi.org/10.3233/jifs-182706 doi: 10.3233/jifs-182706
![]() |
[23] |
E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: a gravitational search algorithm, Inf. Sci., 179 (2009), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004 doi: 10.1016/j.ins.2009.03.004
![]() |
[24] |
S. Dhargupta, M. Ghosh, S. Mirjalili, R. Sarkar, Selective opposition based grey wolf optimization, Expert Syst. Appl., 151 (2020), 113389. https://doi.org/10.1016/j.eswa.2020.113389 doi: 10.1016/j.eswa.2020.113389
![]() |
[25] |
S. Zhang, Q. Luo, Y. Zhou, Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method, Int. J. Comput. Intell. Appl., 16 (2017), 1750012. https://doi.org/10.1007/s13042-022-01537-3 doi: 10.1007/s13042-022-01537-3
![]() |
[26] |
M. A. Navarro, D. Oliva, A. Ramos-Michel, D. Zaldivar, B. Morales-Castaneda, M. Perez-Cisneros, An improved multi-population whale optimization algorithm, Int. J. Mach. Learn. Cybern., 13 (2022), 2447–2478. https://doi.org/10.1007/s13042-022-01537-3 doi: 10.1007/s13042-022-01537-3
![]() |
[27] |
S. M. Bozorgi, S. Yazdani, IWOA: An improved whale optimization algorithm for optimization problems, J. Comput. Des. Eng., 6 (2019), 243–259. https://doi.org/10.1016/j.jcde.2019.02.002 doi: 10.1016/j.jcde.2019.02.002
![]() |
[28] | S. A. Rather, N. Sharma, GSA-BBO hybridization algorithm, Int. J. Adv. Res. Sci. Eng., 6 (2017), 596–608. |
[29] |
V. Muthiah-Nakarajan, M. M. Noel, Galactic swarm optimization: a new global optimization metaheuristic inspired by galactic motion, Appl. Soft Comput., 38 (2016), 771–787. https://doi.org/10.1016/j.asoc.2015.10.034 doi: 10.1016/j.asoc.2015.10.034
![]() |
[30] |
D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Glob. Optim., 39 (2007), 459–471. https://doi.org/10.1007/s10898-007-9149-x doi: 10.1007/s10898-007-9149-x
![]() |
[31] | E. Cuevas, M. Gonzalez, D. Zaldivar, M. Perez-Cisneros, G. Garcia, An algorithm for global optimization inspired by collective animal behavior, Discrete Dyn. Nat. Soc., 2012 (2012). https://doi.org/10.1155/2012/638275 |
[32] | X. S. Yang, S. Deb, Cuckoo search via Lévy flights, in 2009 World congress on nature & biologically inspired computing (NaBIC), (2009), 210–214. https://doi.org/10.1109/NABIC.2009.5393690 |
[33] |
M. A. Diaz-Cortes, E. Cuevas, J. Galvez, O. Camarena, A new metaheuristic optimization methodology based on fuzzy logic, Appl. Soft Comput., 61 (2017), 549–569. https://doi.org/10.1016/j.asoc.2017.08.038 doi: 10.1016/j.asoc.2017.08.038
![]() |
[34] |
S. Mirjalili, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl. Based Syst., 89 (2015), 228–249. https://doi.org/10.1016/j.knosys.2015.07.006 doi: 10.1016/j.knosys.2015.07.006
![]() |
[35] |
E. Mezura-Montes, C. A. Coello Coello, An empirical study about the usefulness of evolution strategies to solve constrained optimization problems, Int. J. Gener. Syst., 37 (2008), 443–473. https://doi.org/10.1080/03081070701303470 doi: 10.1080/03081070701303470
![]() |
[36] |
S. Gupta, K. Deep, H. Moayedi, L. K. Foong, A. Assad, Sine cosine grey wolf optimizer to solve engineering design problems, Eng. Comput., 37 (2021), 3123–3149. https://doi.org/10.1007/s00366-020-00996-y doi: 10.1007/s00366-020-00996-y
![]() |
[37] | N. Mittal, U. Singh, B. S. Sohi, Modified grey wolf optimizer for global engineering optimization, Appl. Comput. Intell. Soft Comput., 2016 (2016). https://doi.org/10.1155/2016/7950348 |
[38] |
F. Yan, X. Xu, J. Xu, Grey Wolf Optimizer With a Novel Weighted Distance for Global Optimization, IEEE Access, 8 (2020), 120173–120197. https://doi.org/10.1109/ACCESS.2020.3005182 doi: 10.1109/ACCESS.2020.3005182
![]() |
[39] |
R. Zheng, H. M. Jia, L. Abualigah, Q. X. Liu, S. Wang, An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems, Math. Biosci. Eng., 19 (2022), 3994–4037. https://doi.org/10.3934/mbe.2022184 doi: 10.3934/mbe.2022184
![]() |
[40] |
S. Li, H. Chen, M. Wang, A. A. Heidari, S. Mirjalili, Slime mould algorithm: a new method for stochastic optimization, Future Gener. Comput. Syst, 111 (2020), 300–323. https://doi.org/10.1016/j.future.2020.03.055 doi: 10.1016/j.future.2020.03.055
![]() |
[41] |
E. H. Houssein, N. Neggaz, M. E. Hosney, W. M. Mohamed, M. Hassaballah, Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities, Neural Comput. Appl., 33 (2021), 13601–13618. https://doi.org/10.1007/s00521-021-05991-y doi: 10.1007/s00521-021-05991-y
![]() |
[42] |
W. Long, J. Jiao, X. Liang, S. Cai, M. Xu, A random opposition-based learning grey wolf optimizer, IEEE Access, 7 (2019), 113810–113825. https://doi.org/10.1109/ACCESS.2019.2934994 doi: 10.1109/ACCESS.2019.2934994
![]() |
[43] |
S. Wang, K. Sun, W. Zhang, H. Jia, Multilevel thresholding using a modified ant lion optimizer with opposition-based learning for color image segmentation, Math. Biosci. Eng., 18 (2021), 3092–3143. https://doi.org/10.3934/mbe.2021155 doi: 10.3934/mbe.2021155
![]() |
[44] |
U. KILIC, E. S. ESSIZ, M. K. KELES, Binary anarchic society optimization for feature selection, Romanian J. Inf. Sci. Technol., 26 (2023), 351–364. https://doi.org/10.1080/00207721.2021.1927236 doi: 10.1080/00207721.2021.1927236
![]() |
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