# A Novel Bat Algorithm Based on Differential Operator and Lévy Flights Trajectory

Authors: Jian Xie; Yongquan Zhou; Huan Chen
Journal: Computational Intelligence and Neuroscience
 (2013)

---

## Abstract

Aiming at the phenomenon of slow convergence rate and low accuracy of bat algorithm, a novel bat algorithm based on differential operator and Lévy flights trajectory is proposed. In this paper, a differential operator is introduced to accelerate the convergence speed of proposed algorithm, which is similar to mutation strategy “DE/best/2” in differential algorithm. Lévy flights trajectory can ensure the diversity of the population against premature convergence and make the algorithm effectively jump out of local minima. 14 typical benchmark functions and an instance of nonlinear equations are tested; the simulation results not only show that the proposed algorithm is feasible and effective, but also demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space.


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## Body

## 1. Introduction

Nowadays, since the evolutionary algorithm can solve some problem that the traditional optimization algorithm cannot do easy, the evolutionary algorithms are widely applied in different fields, such as the management science, engineering optimization, scientific computing. More and more modern metaheuristic algorithms inspired by nature or social phenomenon are emerging and they become increasingly popular, for example, particles swarms optimization (PSO) [1], firefly algorithm (FA) [2, 3], artificial chemical reaction optimization algorithm (ACROA) [4], glowworm swarms optimization (GSO) [5], invasive weed optimization (IWO) [6], differential evolution (DE) [7–9], bat algorithm (BA) [2, 10], and so on [11–15]. Some researchers have proposed their hybrid versions by combining two or more algorithms.Bat Algorithm (BA) is a novel metaheuristic optimization algorithm based on the echolocation behaviour of microbats, which was proposed by Yang in 2010 [2, 10]. This algorithm gradually aroused people’s close attention, and which is increasingly applied to different areas. Tsai et al. (2011) proposed an improved EBA to solve numerical optimization problems [16]. A multiobjective bat algorithm (MOBA) is proposed by Yang (2011) [17], which is first validated against a subset of test functions, and then applied to solve multiobjective design problems such as welded beam design. In 2012, Bora et al. applied BA to solve the Brushless DC Wheel Motor Problem [18]. Although the basic BA has remarkable property compared against several traditional optimization methods, the phenomenon of slow convergence rate and low accuracy still exists. Therefore, in this paper, we put forward an improved bat algorithm based on differential operator and Lévy flights trajectory (DLBA), the purpose is to improve the convergence rate and precision of bat algorithm. At the end of this paper, we tested 14 typical benchmark functions and applied them to solve nonlinear equations; the simulation results not only showed that the proposed algorithm is feasible and effective, which is more robust, but also demonstrated the superior approximation capabilities in high-dimensional space.The rest of this study is organized as follows. In Section2, the basic bat algorithm and Lévy flights were described. In Section 3, we gave the design framework of DLBA. The implementation and comparison of improved algorithm are presented in Section 4. Finally, we concluded this paper in Section 5.


## 2. Bat Algorithm, Lévy Flights, and Nonlinear Equations

### 2.1. Behaviour of Microbats

Most of microbats have advanced capability of echolocation. These bats can emit a very loud and short sound pulse; the echo that reflects back from the surrounding objects is received by their extraordinary big auricle. Then, this feedback information of echo is analyzed in their subtle brain. They not only can discriminate direction for their own flight pathway according to the echo, but also can distinguish different insects and obstacles to hunt prey and avoid a collision effectively in the day or night.


### 2.2. Bat Algorithm (see [10])

First of all, let us briefly review the basics of the BA for single-objective optimization. In the basic BA developed by Yang in 2010, in order to propose the bat algorithm inspired by the echolocation characteristics of microbats, the following approximate or idealised rules were used.IR1. All bats use echolocation to sense distance, and they also “know” the difference between food/prey and background barriers in some magical way.IR2. Bats fly randomly with velocity vi at position xi with a fixed frequency fmin, varying wavelength λ, and loudness A0 to search for prey. They can automatically adjust the wavelength (or frequency) of their emitted pulses and adjust the rate of pulse emission r∈[0,1], depending on the proximity of their target.IR3. Although the loudness can vary in many ways, we assume that the loudness varies from a large (positive) A0 to a minimum constant value Amin.In addition, for simplicity, they also use the following approximations: in general, the frequencyf in a range [fmin,fmax] corresponds to a range of wavelengths [λmin,λmax]. In fact, they just vary in the frequency while fixed in the wavelength λ and assume f∈[0,fmax] in their implementation. This is because λ and f are related due to the fact that λf=v is constant.In simulations, they use virtual bats naturally to define the updated rules of their positionsxi and velocities vi in a D-dimensional search space. The new solutions xit and velocities vit at time step t are given by
(1)fi=fmin+(fmax-fmin)β,vit=vit-1+(xit-x*)fi,xit=xit-1+vit,
where β∈[0,1] is a random vector drawn from a uniform distribution. Here,  x* is the current global best location (solution) which is located after comparing all the solutions among all the n bats.For the local search part, once a solution is selected among the current best solutions, a new solution for each bat is generated locally using random walk:(2)xnew=xold+εAt,
where ε∈[-1,1] is a random number, while At=〈Ait〉 is the average loudness of all the bats at this time step.Furthermore, the loudnessAi and the rate ri of pulse emission have to be updated accordingly as the iterations proceed. These formulas are
(3)Ait+1=αAit,(4)rit+1=ri0[1-exp(-γt)],
where α and γ are constants.Based on these approximations and idealization, the basic steps of the bat algorithm can be summarized in the Pseudocode1.Pseudocode 1:
Objective functionf(x),x=[x1,x2,…,xd]T
Initialize the bat populationxi(i=1,2,…,n) and vi
Define pulse frequencyfi at xi initialize pulse rates ri and the loudness Ai
While (t< Max number of iterations)
Generate new solutions by adjusting frequency,
and updating velocities and locations/solutions (1)
if (rand>ri)
Select a solution among the best solutions
Generate a local solution around the selected best solution
end if
Generate a new solution by flying randomly
if (rand<Ai & f(xi)<f(x*))
Accept the new solutions
Increaseri and reduce Ai
end if
Rank the bats and find the current bestx*
end while
Postprocess results and visualization


### 2.3. Lévy Flights

Lévy flights are Markov processes, which differ from regular Brownian motion, whose individual jumps have lengths that are distributed with the probability density function (PDF)λ(x) decaying at large x as λ(x)=|x|-1-α with 0<α<2. Due to the divergence of their variance, λ(x)≃x|-1-α, extremely long jumps may occur, and typical trajectories are self-similar, on all scales showing clusters of shorter jumps interspersed by long excursions [19]. Lévy flight has the following properties [20]: (1)
Stability: distribution of the sum of independent identically distributed stable random variables equal to distribution of each variable.(2)
Power law asymptotics (“heavy tails”).(3)
Generalized Central Limit Theorem: The central limit theorem states that the sum of a number of independent and identically distributed (i.i.d.) random variables with finite variances will tend to a normal distribution as the number of variables grows.(4)
Which has an infinite variance with an infinite mean value.Due to the these remarkable properties of stable distributions, it is now believed that the Lévy statistics provide a framework for the description of many natural phenomena in physical, chemical, biological, and economical systems from a general common point of view.Furthermore, various studies have shown that the flight behaviour of many animals and insects has demonstrated the typical characteristics of Lévy lights. A recent study by Reynolds and Frye shows that fruit flies, orDrosophila melanogaster, explore their landscape using a series of straight flight paths punctuated by a sudden 90° turn, leading to a Lévy flight-style intermittent scale-free search pattern [21]. Studies on human behaviour such as the Ju/’hoansi hunter-gatherer foraging patterns also show the typical feature of Lévy flights [22]. The conclusion that light is related to Lévy flights is proposed by Barthelemy et al. (2008) [23]. The study by Mercadier et al. shows that the Lévy flights of photons in hot atomic vapours (2009) [24]. Subsequently, such behaviour has been applied to optimization and optimal search, and preliminary results show its promising capability.


### 2.4. Description of the Nonlinear Equations (see [25])

The general form of nonlinear equations with real variables is described as follows:(5)gi(X)=0,i=1,2,…,n,
where X=(x1,x2,…,xn)∈D⊂Rn, D={(x1,x2,…,xn)∣ai≤xi≤bi,i=1,2,…,n}.In solving nonlinear equations process for DLBA, the fitness function can be constructed by(6)G(X)=∑i=1n|gi(x)|.
So, the solving of nonlinear equations can be translated into an optimization problem in domain D:
(7)minG(X)s.t(x1,x2,…,xn)∈D.
Consequently, the optimal value of (7) is exactly the solution of (5).


## 2.1. Behaviour of Microbats

Most of microbats have advanced capability of echolocation. These bats can emit a very loud and short sound pulse; the echo that reflects back from the surrounding objects is received by their extraordinary big auricle. Then, this feedback information of echo is analyzed in their subtle brain. They not only can discriminate direction for their own flight pathway according to the echo, but also can distinguish different insects and obstacles to hunt prey and avoid a collision effectively in the day or night.


## 2.2. Bat Algorithm (see [10])

First of all, let us briefly review the basics of the BA for single-objective optimization. In the basic BA developed by Yang in 2010, in order to propose the bat algorithm inspired by the echolocation characteristics of microbats, the following approximate or idealised rules were used.IR1. All bats use echolocation to sense distance, and they also “know” the difference between food/prey and background barriers in some magical way.IR2. Bats fly randomly with velocity vi at position xi with a fixed frequency fmin, varying wavelength λ, and loudness A0 to search for prey. They can automatically adjust the wavelength (or frequency) of their emitted pulses and adjust the rate of pulse emission r∈[0,1], depending on the proximity of their target.IR3. Although the loudness can vary in many ways, we assume that the loudness varies from a large (positive) A0 to a minimum constant value Amin.In addition, for simplicity, they also use the following approximations: in general, the frequencyf in a range [fmin,fmax] corresponds to a range of wavelengths [λmin,λmax]. In fact, they just vary in the frequency while fixed in the wavelength λ and assume f∈[0,fmax] in their implementation. This is because λ and f are related due to the fact that λf=v is constant.In simulations, they use virtual bats naturally to define the updated rules of their positionsxi and velocities vi in a D-dimensional search space. The new solutions xit and velocities vit at time step t are given by
(1)fi=fmin+(fmax-fmin)β,vit=vit-1+(xit-x*)fi,xit=xit-1+vit,
where β∈[0,1] is a random vector drawn from a uniform distribution. Here,  x* is the current global best location (solution) which is located after comparing all the solutions among all the n bats.For the local search part, once a solution is selected among the current best solutions, a new solution for each bat is generated locally using random walk:(2)xnew=xold+εAt,
where ε∈[-1,1] is a random number, while At=〈Ait〉 is the average loudness of all the bats at this time step.Furthermore, the loudnessAi and the rate ri of pulse emission have to be updated accordingly as the iterations proceed. These formulas are
(3)Ait+1=αAit,(4)rit+1=ri0[1-exp(-γt)],
where α and γ are constants.Based on these approximations and idealization, the basic steps of the bat algorithm can be summarized in the Pseudocode1.Pseudocode 1:
Objective functionf(x),x=[x1,x2,…,xd]T
Initialize the bat populationxi(i=1,2,…,n) and vi
Define pulse frequencyfi at xi initialize pulse rates ri and the loudness Ai
While (t< Max number of iterations)
Generate new solutions by adjusting frequency,
and updating velocities and locations/solutions (1)
if (rand>ri)
Select a solution among the best solutions
Generate a local solution around the selected best solution
end if
Generate a new solution by flying randomly
if (rand<Ai & f(xi)<f(x*))
Accept the new solutions
Increaseri and reduce Ai
end if
Rank the bats and find the current bestx*
end while
Postprocess results and visualization


## 2.3. Lévy Flights

Lévy flights are Markov processes, which differ from regular Brownian motion, whose individual jumps have lengths that are distributed with the probability density function (PDF)λ(x) decaying at large x as λ(x)=|x|-1-α with 0<α<2. Due to the divergence of their variance, λ(x)≃x|-1-α, extremely long jumps may occur, and typical trajectories are self-similar, on all scales showing clusters of shorter jumps interspersed by long excursions [19]. Lévy flight has the following properties [20]: (1)
Stability: distribution of the sum of independent identically distributed stable random variables equal to distribution of each variable.(2)
Power law asymptotics (“heavy tails”).(3)
Generalized Central Limit Theorem: The central limit theorem states that the sum of a number of independent and identically distributed (i.i.d.) random variables with finite variances will tend to a normal distribution as the number of variables grows.(4)
Which has an infinite variance with an infinite mean value.Due to the these remarkable properties of stable distributions, it is now believed that the Lévy statistics provide a framework for the description of many natural phenomena in physical, chemical, biological, and economical systems from a general common point of view.Furthermore, various studies have shown that the flight behaviour of many animals and insects has demonstrated the typical characteristics of Lévy lights. A recent study by Reynolds and Frye shows that fruit flies, orDrosophila melanogaster, explore their landscape using a series of straight flight paths punctuated by a sudden 90° turn, leading to a Lévy flight-style intermittent scale-free search pattern [21]. Studies on human behaviour such as the Ju/’hoansi hunter-gatherer foraging patterns also show the typical feature of Lévy flights [22]. The conclusion that light is related to Lévy flights is proposed by Barthelemy et al. (2008) [23]. The study by Mercadier et al. shows that the Lévy flights of photons in hot atomic vapours (2009) [24]. Subsequently, such behaviour has been applied to optimization and optimal search, and preliminary results show its promising capability.


## 2.4. Description of the Nonlinear Equations (see [25])

The general form of nonlinear equations with real variables is described as follows:(5)gi(X)=0,i=1,2,…,n,
where X=(x1,x2,…,xn)∈D⊂Rn, D={(x1,x2,…,xn)∣ai≤xi≤bi,i=1,2,…,n}.In solving nonlinear equations process for DLBA, the fitness function can be constructed by(6)G(X)=∑i=1n|gi(x)|.
So, the solving of nonlinear equations can be translated into an optimization problem in domain D:
(7)minG(X)s.t(x1,x2,…,xn)∈D.
Consequently, the optimal value of (7) is exactly the solution of (5).


## 3. DLBA

Inspired by Yang’s method, we propose an improved bat algorithm based on differential operator and Lévy-flights trajectory (DLBA) based on the basic structure of BA and re-estimate the characters used in the original BA. In DLBA, not only the movement of the bat is quite different from the original BA, but also the local search process is different.In DLBA, the frequency fluctuates up and down, which can change self-adaptively, and the differential operator is introduced, which is similar to the mutation operation of DE, the frequencyf is similar to the scale factor F of DE/best/2. So, the frequency updated formulae of a bat are defined as follows:
(8)f1it=((f1,min-f1,max)tnt+f1,max)β1,(9)f2it=((f2,max-f2,min)tnt+f2,min)β2,
where β1,β2∈[0,1] is a random vector drawn from a uniform distribution, f1,max=f2,max,f1,min=f2,min, nt is a fixed parameter. In DLBA, the positionxitof each bat individual are updated with (10), which is different from original BA. This can preferably incorporate the echolocation characteristics of microbats:
(10)xit+1=xbestt+f1it(xr1t-xr2t)+f2it(xr3t-xr4t),
where xbestt is the current global best location (solution) which is located after comparing all the solutions among all the n bats in t generation, xrit is the bat individual in the bat swarm, and this can be achieved by randomization.In addition, Lévy flight haves the prominent properties increase the diversity of population, sequentially, which can make the algorithm effectively jump out of the local optimum. So, we let these bats perform the Lévy flights with (11) before the position updating:
(11)xit=x^it-1+μsign[rand-0.5]⊕Levy,
where μ is a random parameter drawn from a uniform distribution, sign ⊕ means entrywise multiplications, rand∈[0,1], and random step length Levy obeys Lévy distribution:
(12)Levy~u=t-λ,(1<λ≤3).On the other hand, each bat should have different values of loudness and pulse emission rate, while the rate of pulse emission is relatively low and the loudness is relatively high. During the search process, the loudness usually decreases, while the rate of pulse emission increases. Bats’ position variation is influenced by the pulse emission rate and loudness as well. Firstly, pulse emission rateri causes fluctuation of position using (2); sequentially, more and more new position can be explored. Secondly, loudness Ai is designed to strengthen local search and to guide bats find better solutions:
(13)xnew=xbest+ηrt,
where η∈[-1,1] is a random parameter, and xbest is the current global best location (solution) in whole bats swarm. While rt=〈rit〉 is the average pulse emission rate of all the bats at this generation.The new rate of pulse emissionrit and loudness Ait at time step t are given by
(14)rit+1=rit(tnt)3,
where rit is time varying; their loudness and emission rates will be updated only if the best solution of the current generation is better than the best solution of last generation, which means that these bats are moving towards the optimal solution. The pseudocode of DLBA can be depicted as in Pseudocode 2.Pseudocode 2:
Input:Objective function f(x),x=[x1,x2,…,xd]T; algorithm’s parameters n,fmin,fmax,α,nt;
BEGIN
Fori=1 To n do
Initializexit, ri0, Ai0;
Calculating the initial fitness;
End For
While (t<itermax)
Update position use formula (8)~(12);
Evaluate fitnessf(xi) to find xbest;
Fori=1 To n do
If(rand >ri)
Generate axnew around the selected best solution use formula (2);
End If
End For
Evaluate fitnessf(xnew);
Fori=1 To n do
If(rand <Ai)
Generate axnew around the selected best solution use formula (13);
End If
End For
Evaluate fitnessf(xnew);
If  (f(xbestt+1)<f(xbestt))
Updaterit, Ait use formula (3), (14);
End If
t = t+1;
End While
END
Output:
x
best
,
fitnes
s
min;


## 4. The Simulation and Analysis

### 4.1. Parametric Studies

The proposed DLBA is implemented in MATLAB, simulation platform: CPU Intel Xeon E5405@2.00 GHz; OS: Microsoft Windows Server 2003 Enterprise Edition SP2; RAM: 1 GHz; Matlab Version: R2009a. The parameter setting for BA and DLBA are listed in Table1, the parameters of BA are recommended in the original article.Table 1
The parameter set of BA and DLBA.
Algorithms
n
f
min
f
max
A
i
0
r
i
0
α
γ
n
t
BA
40
0
100
(
1,2
)
(
0,0.1
)
0.9
0.9
—
DLBA
40
0
1
(
1,2
)
(
0,0.1
)
0.9
—
5000The stopping criterion can be defined in many ways. We adopt two terminated criteria: we can use a given tolerance (Tol=1.0e-5) for a test function that have certain minimum value in theory; on the contrary, each simulation run terminates when a certain number of function evaluations (FEs) have been reached. In this paper, FEs could be obtained by population size multiplied by the number of iteration. In our experiment, FEs = 24000, BA (= 300*40*2); DLBA (= 200*40*3).


### 4.2. Benchmark Test Function

In order to validate the validity of DLBA, we selected the 14 benchmark functions to experimentize. The benchmark set include unimodal, multimodal, high-dimensional, and low-dimensional unconstrained optimization benchmark functions, wheref1–f3 are unimodal functions, f4–f14 are multimodal functions; f1–f9 have certain theoretical minimum and f10–f14 have uncertain theoretical minimum.(
1
)  f1: sphere function (the first function of De Jong’s test set),
(15)f(x)=∑i=1nxi2,-10≤xi≤10.
Here, n is dimensionality; this function has a global minimum fmin=0 at x*=(0,0,…,0).(
2
)
f
2: Schwegel’s problem 2.22,
(16)f(x)=∑i=1n|xi|+∏i=1n|xi|,-10≤xi≤10,
whose global minimum is obviously fmin=0 at x*=(0,0,…,0).(
3
)
f
3: Rosenbrock function,
(17)f(x)=∑i=1n-1[(xi-1)2+100(xi+1-xi2)2],-2.408≤xi≤2.408,
which has a global minimum fmin=0 at x*=(1,1,…,1).(
4
)
f
4: Eggcrate function,
(18)f(x,y)=x2+y2+25(sin2x+sin2y),(x,y)∈[-2π,2π].
This 2-dimensional test function obviously gets the global minimum fmin=0 at (0, 0).(
5
)
f
5: Ackley’s function,
(19)f(x)=-20exp[-151n∑i=1nxi2]-exp[1n∑i=1ncos(2πxi)]+20+e,-30≤x≤30.
This function has a global minimum fmin=0 at x*=(0,0,…,0), which is a multimodal function.(
6
)
f
6: Griewangk’s function,
(20)f(x)=14000∑i=1nxi2-∏i=1ncos(xii)+1,-600≤xi≤600.
Its global minimum equal fmin=0 is obtainable for x*=(0,0,…,0), the number of local minima for arbitrary n is unknown, but in the two-dimensional case there are some 500 local minima.(
7
)
f
7: Salomon’s function,
(21)f(x)=-cos(2π∑i=1nxi2)+0.1∑i=1nxi2+1,-5≤xi≤5,
which has a global minimum fmin=0 at x*=(0,0,…,0), and is a multimodal function.(
8
)
f
8: Rastrigin’s function. 								(22)f(x)=10n+∑i=1n[xi2-10cos(2πxi)],-5.12≤xi≤5.12,
whose global minimum is fmin=0 at x*=(0,0,…,0); for n=2, there are about 50 local minimizers arranged in a lattice-like configuration.(
9
)
f
9: Zakharov’s function,
(23)f(x)=∑i=1nxi2+(12∑i=1nixi)2+(12∑i=1nixi)4,-10≤xi≤10,
whose global minimum is fmin=0 at x*=(0,0,…,0); it is a multimodal function as well.(
10
)
f
10: Easom’s function,
(24)f(x,y)=-cos(x)cos(y)exp[-(x-π)2+(y-π)2],-10≤x,y≤10,
whose global minimum is fmin=-1 at (π,π); it has many local minima.(
11
)
f
11: Schwegel’s function,
(25)f(x)=-∑i=1nxisin(|xi|),-500≤xi≤500,
whose global minimum is fmin≈-418.9829n occuring at x*≈(420.9687,…,420.9687).(
12
)
f
12: Shubert’s function,
(26)f(x,y)=[∑i=15icos(i+(i+1)x)]·[∑i=15icos(i+(i+1)y)],-10≤x,y≤10.
The number of local minima for this problem is not known but for n=2, the function has 760 local minima, 18 of which are global with fmin≈-186.7309.(
13
)
f
13: Xin-She Yang’s function,
(27)f(x)=-(∑i=1n|xi|)exp(-∑i=1nxi2),-10≤xi≤10,
which has multiple global minima, for example, for n=2, it has 4 equal minima fmin=-1/e≈-0.6065 at (0.5,0.5), (0.5-0.5), (-0.5,0.5), and (−0.5, −0.5).(
14
)
f
14: “Drop Wave” function,
(28)f(x)=-1+cos(12∑i=1nxi2)(1/2)(∑i=1nxi2)+2,-5.12≤xi≤5.12.
This two-variable function is a multimodal test function, whose global minimum is fmin=-1 occurs at x*=(0,0,…,0).


### 4.3. Comparison of Experimental Results

The 2D landscape of Schwegel’s function is shown in Figure1, and this global minimum can be found after about 720 FEs for 40 bats after 6 iterations as shown in Figures 2, 3, and 4.Figure 1
The landscape of Schwegel’s function.Figure 2
The locations of 40 bats in the initial phase.Figure 3
The locations of 40 bats after six iterations.Figure 4
The locations of 40 bats after fifteen iterations.We adopt different terminated criteria aiming at different benchmark function, we perform 100 times independently for each test function, and the record is given in Tables2 and 3.Table 2
Comparison of BA and DLBA for several test functions under the first terminated criteria.
TC
BF
Method
Fitness
Iteration
Min
Mean
Max
Std
Min
Mean
Max
Tol =1.0e-5, itermax = 200
f
1 (D=30)
DLBA
4.30568008
E
-
08
4.80356915
E
-
06
9.96163402
E
-
06
3.02060716
E
-
06
10
17.6
26
BA
56.81732462
130.61959271
223.82694528
37.22960351
200
200
200
f
2 (D=20)
DLBA
5.66761254
E
-
07
6.33091639
E
-
06
9.88929757
E
-
06
2.48867687
E
-
06
21
29.4
37
BA
22.77136624
93.35062167
2733.46090499
269.41410301
200
200
200
f
3 (D=10)
DLBA
5.66716240
7.39029403
8.28857171
0.37001430
200
200
200
BA
82.24908050
294.95948479
640.76052284
109.67161818
200
200
200
f
4 (D=2)
DLBA
6.23475433
E
-
09
3.93352701
E
-
06
9.97022879
E
-
06
2.77208752
E
-
06
6
10
20
BA
1.05265280
E
-
04
0.20705750
1.00771980
0.23116047
200
200
200
f
5 (D=5)
DLBA
4.71737180
E
-
07
6.40969812
E
-
06
9.96088247
E
-
06
2.71187009
E
-
06
16
25.6
39
BA
1.15883977
3.02092450
5.86431360
0.69052427
200
200
200
f
6 (D=5)
DLBA
1.38176777
E
-
07
5.01027883
E
-
06
9.92998256
E
-
06
2.77338474
E
-
06
11
18.4
56
BA
0.06234957
8.42851961
29.90707979
6.12812390
200
200
200
f
7 (D=5)
DLBA
7.78490674
E
-
07
6.57288799
E
-
06
9.97486019
E
-
06
2.36637154
E
-
06
18
46
114
BA
0.09987344
0.15318115
0.30215065
0.04923496
200
200
200
f
8 (D=5)
DLBA
5.99983281
E
-
08
4.47052788
E
-
06
9.85695441
E
-
06
2.81442645
E
-
06
15
33
87
BA
7.24547772
17.11077150
25.90638177
4.37190073
200
200
200
f
9 (D=5)
DLBA
6.65070016
E
-
09
3.88692603
E
-
06
9.69841027
E
-
06
2.75444981
E
-
06
10
15.1
23
BA
0.11106843
1.75623424
8.48211975
1.41343722
200
200
200Table 3
Comparison of BA and DLBA for several test functions under the second terminated condition.
TC
BF
Method
Fitness
Min
Mean
Max
Std
FEs
f
10 (D=2)
DLBA
−1
−1
−1
9.06493304
E
-
17
BA
−0.99964845
−0.98245546
−0.90221232
0.01759602
f
11 (D=2)
DLBA
−418.98288727
−415.83523725
−300.54455266
15.53948263
BA
−418.98287874
−406.98044144
−328.10751671
22.54615132
f
12 (D=5)
DLBA
−186.73090883
−186.73090883
−186.73090883
4.31613544
E
-
13
BA
−186.67541533
−184.01677767
−174.95599301
2.26333511
f
13 (D=2)
DLBA
−0.60653066
−0.60653066
−0.60653066
8.63947249
E
-
16
BA
−0.60652631
−0.60411559
−0.59656608
2.18888608
E
-
03
f
14 (D=5)
DLBA
−1
−1
−1
0
BA
−0.93624514
−0.80454167
−0.56977584
0.10270830From Table2, we can see that the DLBA performs much better than the basic bat algorithm, which converges much faster than BA under the fixed accuracy Tol=1.0e-5. In our experiment, for the function f4, we proposed that an algorithm only costs 6 generations under the best situation, and the average generations is 10. Furthermore, for the first group of test functions f1–f9, the DLBA averagely expend 43.9 generations when each function attains its terminated criteria; however, the BA needs 200 generations invariably, and the convergence speed of DLBA advances 298.57% times. On other hand, the DLBA which obtained the accuracy of the solution is much superior to BA, which is more approximate to the theoretical value. In a word, it demonstrated that DLBA has fast convergence rate and high precision of the solutions.In Table3, the five functions (f10–f14) independently runs 100 times under the 24000 Fes; we can clearly see the precision of DLBA is obviously superior to the bat algorithm. Some benchmark functions can easily attain the theoretical optimal value. In addition, the standard deviation of DLBA is relatively low. It shows that DLBA has superior approximation ability.Observe Tables2 and 3, no matter high-dimensional or low-dimensional, we can find that DLBA can quickly converge to the global minima. Furthermore, Figures 5, 6, 7, 8, and 9 show the convergence curves for some of the functions from a particular run of DLBA and BA, which end at 200 generations. The adaptive scheme generally converges faster than the basic scheme. Here, we select f2 (D = 20), f4 (D = 2), f7 (D = 5), f11 (D = 2), and f13 (D = 2).Figure 5
Convergence curves for the functionf2.Figure 6
Convergence curves for the functionf4.Figure 7
Convergence curves for the functionf7.Figure 8
Convergence curves for the functionf11.Figure 9
Convergence curves for the functionf13.DLBA not only has superior approximation ability in low-dimensional space, but also has excellent global search ability in high-dimensional situation. Table4 is the experimental result that DLBA performs 50 times independently under the high-dimensional situation. As shown in Table 4, we can be conscious that the DLBA is effective under the multidimensional condition, and acquired solution has higher accuracy, even approximate the theoretical value.Table 4
Comparison of DLBA and BA under the high-dimensional situation.
BF
Method
Fitness
Min
Mean
Max
Std
f
6 (D=128)
DLBA
0
0
0
0
BA
2587.38759264
2811.40649000
2966.56723398
93.96861133
f
9 (D=256)
DLBA
2.66885939
E
-
94
5.12093542
E
-
69
2.56023720
E
-
67
3.62071553
E
-
68
BA
7503.39306546
8256.61302714
11506.79192998
613.37556609
f
8 (D=320)
DLBA
0
0
0
0
BA
4468.59039467
4723.44624200
5008.57109760
131.18818777
f
14 (D=512)
DLBA
−1
−1
−1
0
BA
-
1.48358700
E
-
03
-
1.31084173
E
-
03
-
1.20435493
E
-
03
5.99288716
E
-
05
f
1 (D=1024)
DLBA
2.63790469
E
-
109
4.73742970
E
-
87
2.03861481
E
-
85
2.89031313
E
-
86
BA
18463.48950157
19752.26686813
21239.23111285
567.27878361Figures10, 11, 12, 13, and 14 are the distribution map of optimal fitness; that is the selected functions independently perform 50 times under the multidimensional situation. Figure 13 show that two “straight line”, we can see in the figure that DLBA reaches the global optimum(−1), however, BA fluctuates around 0. In order to display the fact of fluctuation, we magnify the two “straight line”, and the amplifying effect are depicted in Figures 15 and 16. According to the experimental results which are obtained from selected test functions, DLBA presents higher precision than the original BA on minimizing the outcome as the optimization goal.Figure 10
Distribution of optimal fitness forf6 (D = 128).Figure 11
Distribution of optimal fitness forf9 (D = 256).Figure 12
Distribution of optimal fitness forf8 (D = 320).Figure 13
Distribution of optimal fitness forf14 (D = 512).Figure 14
Distribution of optimal fitness forf1 (D = 1024).Figure 15
Details of BA line in Figure12.Figure 16
Details of DLBA line in Figure12.


### 4.4. An Application for Solving Nonlinear Equations

Interval Arithmetic Benchmark (IAB). We consider one benchmark problem proposed from interval arithmetic, the benchmark consists of the following system of [25]:
(29)0=x1-0.25428722-0.18324757x4x3x9,0=x2-0.37842197-0.16275449x1x10x6,0=x3-0.27162577-0.16955071x1x2x10,0=x4-0.19807914-0.15585316x7x1x6,0=x5-0.44166728-0.19950920x7x6x3,0=x6-0.14654113-0.18922793x8x5x10,0=x7-0.42937161-0.21180486x2x5x8,0=x8-0.07056438-0.17081208x1x7x6,0=x9-0.34504906-0.19612740x10x6x8,0=x10-0.42651102-0.21466544x4x8x1.Some of the solutions obtained as well as the function values (which represent the values of the system’s equations obtained by replacing the variable values) are presented in Table5. From Table 5, we can obviously observe that the function value of each equation solved by DLBA is superior to which solved by EA [25], depending on the statistics of the 40 function values, the precision of function values is enhanced 5.199820E + 06 times by DLBA. The convergence curve of DLBA is depicted in Figure 17. Figure 17 show that DLBA has fast convergence rate for solving nonlinear equations. The achieved optimal fitness (the sum of function value with absolute value) in 50 times independent run is depicted in Figure 18. In order to reflect the precision of fitness, we magnify Figure 18, these optimal fitness that less than or equal to 0.001 are plotted in Figure 19. We can found that there are 32 times optimal fitness is less than 0.001.Table 5
Outcome of DLBA for Interval Arithmetic Benchmark And in Comparison with EA.
Method
EA
DLBA
Solutions
Variables values
Functions values
Variables values
Functions values
Sol. 1
0.0464905115
0.2077959240
0.2578335208
7.22997083
E
-
08
0.1013568357
0.2769798846
0.3810968901
-
2.84879905
E
-
07
0.0840577820
0.1876863212
0.2787447331
-
2.63974619
E
-
07
-
0.1388460309
0.3367887114
0.2006720536
3.06212480
E
-
06
0.4943905739
0.0530391321
0.4452522319
7.74692656
E
-
07
-
0.0760685163
0.2223730535
0.1491853777
1.33597657
E
-
06
0.2475819110
0.1816084752
0.4320098178
-
7.87244534
E
-
09
-
0.0170748156
0.0878963860
0.0734062202
3.41255029
E
-
06
0.0003667535
0.3447200366
0.3459672005
3.24064555
E
-
07
0.1481119311
0.2784227489
0.4273251429
-
1.18429644
E
-
06
Sol. 2
0.1224819761
0.1318552790
0.2578332642
-
1.45675448
E
-
07
0.1826200685
0.1964428361
0.3810968252
-
3.41069930
E
-
07
0.2356779803
0.0364987069
0.2787462357
1.19723259
E
-
06
-
0.0371150470
0.2354890155
0.2006687659
-
1.76074071
E
-
08
0.3748181856
0.0675753064
0.4452514819
2.89773434
E
-
07
0.2213311341
0.0739986588
0.1491839999
9.16571896
E
-
08
0.0697813043
0.3607038292
0.4319795315
-
3.01421570
E
-
05
0.0768058043
0.0059182979
0.0734021258
-
4.53845535
E
-
07
-
0.0312153867
0.3767487763
0.3459672388
4.15572858
E
-
07
0.1452667120
0.2811693568
0.4273281275
1.85998012
E
-
06
Sol. 3
0.0633944399
0.1908436653
0.2578328072
-
5.95667763
E
-
07
0.1017426933
0.2767897367
0.3810969084
-
2.39480772
E
-
07
-
0.1051842285
0.3769063436
0.2787447816
-
2.04170456
E
-
07
-
0.0477059943
0.2460900702
0.2006696507
6.71241513
E
-
07
0.4149858326
0.0260337751
0.4452514467
-
4.38810984
E
-
09
0.1215195321
0.0256054760
0.1491841089
1.89336147
E
-
07
0.2539777159
0.1761486401
0.4320126793
2.97845790
E
-
06
0.0843972823
0.1349869851
0.0734028724
7.79163472
E
-
08
-
0.0534132992
0.3986395691
0.3459668311
3.30962522
E
-
09
0.0880998746
0.3383563536
0.4273256312
-
6.46813249
E
-
07
Sol. 4
0.1939820199
0.0603335280
0.2578382574
5.02240252
E
-
06
0.0152114400
0.3633514726
0.3810978088
8.03072038
E
-
07
0.1618654345
0.1097465792
0.2787430938
-
1.98215448
E
-
06
0.0056985809
0.9114653768
0.2006688671
3.21352180
E
-
08
0.1904538879
0.2502358229
0.4452573447
6.19105762
E
-
06
-
0.1623604033
0.3089460561
0.1491746444
-
9.01451453
E
-
06
0.1864448178
0.2428992222
0.4320068557
-
2.61979130
E
-
06
-
0.0449302706
0.1144916285
0.0733954587
-
7.17750570
E
-
06
0.1675935311
0.1774161896
0.3459538936
-
1.27733934
E
-
05
-
0.0274959004
0.4539962587
0.4273210007
-
5.20897654
E
-
06Figure 17
Convergence curves for DLAB.Figure 18
Distribution of optimal fitness for DLAB.Figure 19
Distribution of optimal fitness less than 0.001 in Figure18.


## 4.1. Parametric Studies

The proposed DLBA is implemented in MATLAB, simulation platform: CPU Intel Xeon E5405@2.00 GHz; OS: Microsoft Windows Server 2003 Enterprise Edition SP2; RAM: 1 GHz; Matlab Version: R2009a. The parameter setting for BA and DLBA are listed in Table1, the parameters of BA are recommended in the original article.Table 1
The parameter set of BA and DLBA.
Algorithms
n
f
min
f
max
A
i
0
r
i
0
α
γ
n
t
BA
40
0
100
(
1,2
)
(
0,0.1
)
0.9
0.9
—
DLBA
40
0
1
(
1,2
)
(
0,0.1
)
0.9
—
5000The stopping criterion can be defined in many ways. We adopt two terminated criteria: we can use a given tolerance (Tol=1.0e-5) for a test function that have certain minimum value in theory; on the contrary, each simulation run terminates when a certain number of function evaluations (FEs) have been reached. In this paper, FEs could be obtained by population size multiplied by the number of iteration. In our experiment, FEs = 24000, BA (= 300*40*2); DLBA (= 200*40*3).


## 4.2. Benchmark Test Function

In order to validate the validity of DLBA, we selected the 14 benchmark functions to experimentize. The benchmark set include unimodal, multimodal, high-dimensional, and low-dimensional unconstrained optimization benchmark functions, wheref1–f3 are unimodal functions, f4–f14 are multimodal functions; f1–f9 have certain theoretical minimum and f10–f14 have uncertain theoretical minimum.(
1
)  f1: sphere function (the first function of De Jong’s test set),
(15)f(x)=∑i=1nxi2,-10≤xi≤10.
Here, n is dimensionality; this function has a global minimum fmin=0 at x*=(0,0,…,0).(
2
)
f
2: Schwegel’s problem 2.22,
(16)f(x)=∑i=1n|xi|+∏i=1n|xi|,-10≤xi≤10,
whose global minimum is obviously fmin=0 at x*=(0,0,…,0).(
3
)
f
3: Rosenbrock function,
(17)f(x)=∑i=1n-1[(xi-1)2+100(xi+1-xi2)2],-2.408≤xi≤2.408,
which has a global minimum fmin=0 at x*=(1,1,…,1).(
4
)
f
4: Eggcrate function,
(18)f(x,y)=x2+y2+25(sin2x+sin2y),(x,y)∈[-2π,2π].
This 2-dimensional test function obviously gets the global minimum fmin=0 at (0, 0).(
5
)
f
5: Ackley’s function,
(19)f(x)=-20exp[-151n∑i=1nxi2]-exp[1n∑i=1ncos(2πxi)]+20+e,-30≤x≤30.
This function has a global minimum fmin=0 at x*=(0,0,…,0), which is a multimodal function.(
6
)
f
6: Griewangk’s function,
(20)f(x)=14000∑i=1nxi2-∏i=1ncos(xii)+1,-600≤xi≤600.
Its global minimum equal fmin=0 is obtainable for x*=(0,0,…,0), the number of local minima for arbitrary n is unknown, but in the two-dimensional case there are some 500 local minima.(
7
)
f
7: Salomon’s function,
(21)f(x)=-cos(2π∑i=1nxi2)+0.1∑i=1nxi2+1,-5≤xi≤5,
which has a global minimum fmin=0 at x*=(0,0,…,0), and is a multimodal function.(
8
)
f
8: Rastrigin’s function. 								(22)f(x)=10n+∑i=1n[xi2-10cos(2πxi)],-5.12≤xi≤5.12,
whose global minimum is fmin=0 at x*=(0,0,…,0); for n=2, there are about 50 local minimizers arranged in a lattice-like configuration.(
9
)
f
9: Zakharov’s function,
(23)f(x)=∑i=1nxi2+(12∑i=1nixi)2+(12∑i=1nixi)4,-10≤xi≤10,
whose global minimum is fmin=0 at x*=(0,0,…,0); it is a multimodal function as well.(
10
)
f
10: Easom’s function,
(24)f(x,y)=-cos(x)cos(y)exp[-(x-π)2+(y-π)2],-10≤x,y≤10,
whose global minimum is fmin=-1 at (π,π); it has many local minima.(
11
)
f
11: Schwegel’s function,
(25)f(x)=-∑i=1nxisin(|xi|),-500≤xi≤500,
whose global minimum is fmin≈-418.9829n occuring at x*≈(420.9687,…,420.9687).(
12
)
f
12: Shubert’s function,
(26)f(x,y)=[∑i=15icos(i+(i+1)x)]·[∑i=15icos(i+(i+1)y)],-10≤x,y≤10.
The number of local minima for this problem is not known but for n=2, the function has 760 local minima, 18 of which are global with fmin≈-186.7309.(
13
)
f
13: Xin-She Yang’s function,
(27)f(x)=-(∑i=1n|xi|)exp(-∑i=1nxi2),-10≤xi≤10,
which has multiple global minima, for example, for n=2, it has 4 equal minima fmin=-1/e≈-0.6065 at (0.5,0.5), (0.5-0.5), (-0.5,0.5), and (−0.5, −0.5).(
14
)
f
14: “Drop Wave” function,
(28)f(x)=-1+cos(12∑i=1nxi2)(1/2)(∑i=1nxi2)+2,-5.12≤xi≤5.12.
This two-variable function is a multimodal test function, whose global minimum is fmin=-1 occurs at x*=(0,0,…,0).


## 4.3. Comparison of Experimental Results

The 2D landscape of Schwegel’s function is shown in Figure1, and this global minimum can be found after about 720 FEs for 40 bats after 6 iterations as shown in Figures 2, 3, and 4.Figure 1
The landscape of Schwegel’s function.Figure 2
The locations of 40 bats in the initial phase.Figure 3
The locations of 40 bats after six iterations.Figure 4
The locations of 40 bats after fifteen iterations.We adopt different terminated criteria aiming at different benchmark function, we perform 100 times independently for each test function, and the record is given in Tables2 and 3.Table 2
Comparison of BA and DLBA for several test functions under the first terminated criteria.
TC
BF
Method
Fitness
Iteration
Min
Mean
Max
Std
Min
Mean
Max
Tol =1.0e-5, itermax = 200
f
1 (D=30)
DLBA
4.30568008
E
-
08
4.80356915
E
-
06
9.96163402
E
-
06
3.02060716
E
-
06
10
17.6
26
BA
56.81732462
130.61959271
223.82694528
37.22960351
200
200
200
f
2 (D=20)
DLBA
5.66761254
E
-
07
6.33091639
E
-
06
9.88929757
E
-
06
2.48867687
E
-
06
21
29.4
37
BA
22.77136624
93.35062167
2733.46090499
269.41410301
200
200
200
f
3 (D=10)
DLBA
5.66716240
7.39029403
8.28857171
0.37001430
200
200
200
BA
82.24908050
294.95948479
640.76052284
109.67161818
200
200
200
f
4 (D=2)
DLBA
6.23475433
E
-
09
3.93352701
E
-
06
9.97022879
E
-
06
2.77208752
E
-
06
6
10
20
BA
1.05265280
E
-
04
0.20705750
1.00771980
0.23116047
200
200
200
f
5 (D=5)
DLBA
4.71737180
E
-
07
6.40969812
E
-
06
9.96088247
E
-
06
2.71187009
E
-
06
16
25.6
39
BA
1.15883977
3.02092450
5.86431360
0.69052427
200
200
200
f
6 (D=5)
DLBA
1.38176777
E
-
07
5.01027883
E
-
06
9.92998256
E
-
06
2.77338474
E
-
06
11
18.4
56
BA
0.06234957
8.42851961
29.90707979
6.12812390
200
200
200
f
7 (D=5)
DLBA
7.78490674
E
-
07
6.57288799
E
-
06
9.97486019
E
-
06
2.36637154
E
-
06
18
46
114
BA
0.09987344
0.15318115
0.30215065
0.04923496
200
200
200
f
8 (D=5)
DLBA
5.99983281
E
-
08
4.47052788
E
-
06
9.85695441
E
-
06
2.81442645
E
-
06
15
33
87
BA
7.24547772
17.11077150
25.90638177
4.37190073
200
200
200
f
9 (D=5)
DLBA
6.65070016
E
-
09
3.88692603
E
-
06
9.69841027
E
-
06
2.75444981
E
-
06
10
15.1
23
BA
0.11106843
1.75623424
8.48211975
1.41343722
200
200
200Table 3
Comparison of BA and DLBA for several test functions under the second terminated condition.
TC
BF
Method
Fitness
Min
Mean
Max
Std
FEs
f
10 (D=2)
DLBA
−1
−1
−1
9.06493304
E
-
17
BA
−0.99964845
−0.98245546
−0.90221232
0.01759602
f
11 (D=2)
DLBA
−418.98288727
−415.83523725
−300.54455266
15.53948263
BA
−418.98287874
−406.98044144
−328.10751671
22.54615132
f
12 (D=5)
DLBA
−186.73090883
−186.73090883
−186.73090883
4.31613544
E
-
13
BA
−186.67541533
−184.01677767
−174.95599301
2.26333511
f
13 (D=2)
DLBA
−0.60653066
−0.60653066
−0.60653066
8.63947249
E
-
16
BA
−0.60652631
−0.60411559
−0.59656608
2.18888608
E
-
03
f
14 (D=5)
DLBA
−1
−1
−1
0
BA
−0.93624514
−0.80454167
−0.56977584
0.10270830From Table2, we can see that the DLBA performs much better than the basic bat algorithm, which converges much faster than BA under the fixed accuracy Tol=1.0e-5. In our experiment, for the function f4, we proposed that an algorithm only costs 6 generations under the best situation, and the average generations is 10. Furthermore, for the first group of test functions f1–f9, the DLBA averagely expend 43.9 generations when each function attains its terminated criteria; however, the BA needs 200 generations invariably, and the convergence speed of DLBA advances 298.57% times. On other hand, the DLBA which obtained the accuracy of the solution is much superior to BA, which is more approximate to the theoretical value. In a word, it demonstrated that DLBA has fast convergence rate and high precision of the solutions.In Table3, the five functions (f10–f14) independently runs 100 times under the 24000 Fes; we can clearly see the precision of DLBA is obviously superior to the bat algorithm. Some benchmark functions can easily attain the theoretical optimal value. In addition, the standard deviation of DLBA is relatively low. It shows that DLBA has superior approximation ability.Observe Tables2 and 3, no matter high-dimensional or low-dimensional, we can find that DLBA can quickly converge to the global minima. Furthermore, Figures 5, 6, 7, 8, and 9 show the convergence curves for some of the functions from a particular run of DLBA and BA, which end at 200 generations. The adaptive scheme generally converges faster than the basic scheme. Here, we select f2 (D = 20), f4 (D = 2), f7 (D = 5), f11 (D = 2), and f13 (D = 2).Figure 5
Convergence curves for the functionf2.Figure 6
Convergence curves for the functionf4.Figure 7
Convergence curves for the functionf7.Figure 8
Convergence curves for the functionf11.Figure 9
Convergence curves for the functionf13.DLBA not only has superior approximation ability in low-dimensional space, but also has excellent global search ability in high-dimensional situation. Table4 is the experimental result that DLBA performs 50 times independently under the high-dimensional situation. As shown in Table 4, we can be conscious that the DLBA is effective under the multidimensional condition, and acquired solution has higher accuracy, even approximate the theoretical value.Table 4
Comparison of DLBA and BA under the high-dimensional situation.
BF
Method
Fitness
Min
Mean
Max
Std
f
6 (D=128)
DLBA
0
0
0
0
BA
2587.38759264
2811.40649000
2966.56723398
93.96861133
f
9 (D=256)
DLBA
2.66885939
E
-
94
5.12093542
E
-
69
2.56023720
E
-
67
3.62071553
E
-
68
BA
7503.39306546
8256.61302714
11506.79192998
613.37556609
f
8 (D=320)
DLBA
0
0
0
0
BA
4468.59039467
4723.44624200
5008.57109760
131.18818777
f
14 (D=512)
DLBA
−1
−1
−1
0
BA
-
1.48358700
E
-
03
-
1.31084173
E
-
03
-
1.20435493
E
-
03
5.99288716
E
-
05
f
1 (D=1024)
DLBA
2.63790469
E
-
109
4.73742970
E
-
87
2.03861481
E
-
85
2.89031313
E
-
86
BA
18463.48950157
19752.26686813
21239.23111285
567.27878361Figures10, 11, 12, 13, and 14 are the distribution map of optimal fitness; that is the selected functions independently perform 50 times under the multidimensional situation. Figure 13 show that two “straight line”, we can see in the figure that DLBA reaches the global optimum(−1), however, BA fluctuates around 0. In order to display the fact of fluctuation, we magnify the two “straight line”, and the amplifying effect are depicted in Figures 15 and 16. According to the experimental results which are obtained from selected test functions, DLBA presents higher precision than the original BA on minimizing the outcome as the optimization goal.Figure 10
Distribution of optimal fitness forf6 (D = 128).Figure 11
Distribution of optimal fitness forf9 (D = 256).Figure 12
Distribution of optimal fitness forf8 (D = 320).Figure 13
Distribution of optimal fitness forf14 (D = 512).Figure 14
Distribution of optimal fitness forf1 (D = 1024).Figure 15
Details of BA line in Figure12.Figure 16
Details of DLBA line in Figure12.


## 4.4. An Application for Solving Nonlinear Equations

Interval Arithmetic Benchmark (IAB). We consider one benchmark problem proposed from interval arithmetic, the benchmark consists of the following system of [25]:
(29)0=x1-0.25428722-0.18324757x4x3x9,0=x2-0.37842197-0.16275449x1x10x6,0=x3-0.27162577-0.16955071x1x2x10,0=x4-0.19807914-0.15585316x7x1x6,0=x5-0.44166728-0.19950920x7x6x3,0=x6-0.14654113-0.18922793x8x5x10,0=x7-0.42937161-0.21180486x2x5x8,0=x8-0.07056438-0.17081208x1x7x6,0=x9-0.34504906-0.19612740x10x6x8,0=x10-0.42651102-0.21466544x4x8x1.Some of the solutions obtained as well as the function values (which represent the values of the system’s equations obtained by replacing the variable values) are presented in Table5. From Table 5, we can obviously observe that the function value of each equation solved by DLBA is superior to which solved by EA [25], depending on the statistics of the 40 function values, the precision of function values is enhanced 5.199820E + 06 times by DLBA. The convergence curve of DLBA is depicted in Figure 17. Figure 17 show that DLBA has fast convergence rate for solving nonlinear equations. The achieved optimal fitness (the sum of function value with absolute value) in 50 times independent run is depicted in Figure 18. In order to reflect the precision of fitness, we magnify Figure 18, these optimal fitness that less than or equal to 0.001 are plotted in Figure 19. We can found that there are 32 times optimal fitness is less than 0.001.Table 5
Outcome of DLBA for Interval Arithmetic Benchmark And in Comparison with EA.
Method
EA
DLBA
Solutions
Variables values
Functions values
Variables values
Functions values
Sol. 1
0.0464905115
0.2077959240
0.2578335208
7.22997083
E
-
08
0.1013568357
0.2769798846
0.3810968901
-
2.84879905
E
-
07
0.0840577820
0.1876863212
0.2787447331
-
2.63974619
E
-
07
-
0.1388460309
0.3367887114
0.2006720536
3.06212480
E
-
06
0.4943905739
0.0530391321
0.4452522319
7.74692656
E
-
07
-
0.0760685163
0.2223730535
0.1491853777
1.33597657
E
-
06
0.2475819110
0.1816084752
0.4320098178
-
7.87244534
E
-
09
-
0.0170748156
0.0878963860
0.0734062202
3.41255029
E
-
06
0.0003667535
0.3447200366
0.3459672005
3.24064555
E
-
07
0.1481119311
0.2784227489
0.4273251429
-
1.18429644
E
-
06
Sol. 2
0.1224819761
0.1318552790
0.2578332642
-
1.45675448
E
-
07
0.1826200685
0.1964428361
0.3810968252
-
3.41069930
E
-
07
0.2356779803
0.0364987069
0.2787462357
1.19723259
E
-
06
-
0.0371150470
0.2354890155
0.2006687659
-
1.76074071
E
-
08
0.3748181856
0.0675753064
0.4452514819
2.89773434
E
-
07
0.2213311341
0.0739986588
0.1491839999
9.16571896
E
-
08
0.0697813043
0.3607038292
0.4319795315
-
3.01421570
E
-
05
0.0768058043
0.0059182979
0.0734021258
-
4.53845535
E
-
07
-
0.0312153867
0.3767487763
0.3459672388
4.15572858
E
-
07
0.1452667120
0.2811693568
0.4273281275
1.85998012
E
-
06
Sol. 3
0.0633944399
0.1908436653
0.2578328072
-
5.95667763
E
-
07
0.1017426933
0.2767897367
0.3810969084
-
2.39480772
E
-
07
-
0.1051842285
0.3769063436
0.2787447816
-
2.04170456
E
-
07
-
0.0477059943
0.2460900702
0.2006696507
6.71241513
E
-
07
0.4149858326
0.0260337751
0.4452514467
-
4.38810984
E
-
09
0.1215195321
0.0256054760
0.1491841089
1.89336147
E
-
07
0.2539777159
0.1761486401
0.4320126793
2.97845790
E
-
06
0.0843972823
0.1349869851
0.0734028724
7.79163472
E
-
08
-
0.0534132992
0.3986395691
0.3459668311
3.30962522
E
-
09
0.0880998746
0.3383563536
0.4273256312
-
6.46813249
E
-
07
Sol. 4
0.1939820199
0.0603335280
0.2578382574
5.02240252
E
-
06
0.0152114400
0.3633514726
0.3810978088
8.03072038
E
-
07
0.1618654345
0.1097465792
0.2787430938
-
1.98215448
E
-
06
0.0056985809
0.9114653768
0.2006688671
3.21352180
E
-
08
0.1904538879
0.2502358229
0.4452573447
6.19105762
E
-
06
-
0.1623604033
0.3089460561
0.1491746444
-
9.01451453
E
-
06
0.1864448178
0.2428992222
0.4320068557
-
2.61979130
E
-
06
-
0.0449302706
0.1144916285
0.0733954587
-
7.17750570
E
-
06
0.1675935311
0.1774161896
0.3459538936
-
1.27733934
E
-
05
-
0.0274959004
0.4539962587
0.4273210007
-
5.20897654
E
-
06Figure 17
Convergence curves for DLAB.Figure 18
Distribution of optimal fitness for DLAB.Figure 19
Distribution of optimal fitness less than 0.001 in Figure18.


## 5. Conclusions

Aiming at the phenomenon of slow convergence rate and low accuracy of bat algorithm, we put forward animproved bat algorithm with differential operator and Lévy flights trajectory (DLBA) based on the basic framework of bat algorithm (BA), the purpose is to improve the convergence rate and precision of bat algorithm. In this paper, we define the frequency fluctuations up and down when the bat tracking prey, which influence the bats’ location problem; it is more graphic to simulate the bat’s behavior. Moreover, it can make the algorithm effectively jump out of the local optimum to add Lévy flights and differential operator, the main reason is because Lévy flight has prominent properties in the previously mentioned and the differential operator can guide bats find better solutions, sequentially, increase the convergent speed.In addition, bats’ position variation is influenced by the pulse emission rate and loudness as well. Firstly, pulse emission rate causes update of position, and more and more new position can be explored, consequently, increasing the diversity of population. Secondly, loudness is designed to strengthen local search and to guide bats find better solutions.In this paper, we tested 14 typical benchmark functions and applied them to solve nonlinear equations, the simulation results not only showed that the proposed algorithm is feasible and effective, which is more robust, but also demonstrated the superior approximation capabilities in high-dimensional space. This is not surprising as the aim of developing the new algorithm was to try to use the advantages of existing algorithms and other interesting feature inspired by the fantastic behavior of echolocation of microbats. Numerically speaking, these can be translated into two crucial characteristics of the modern metaheuristics: intensification and diversification. Intensification intends to search around the current best solutions and select the best candidates or solutions, while diversification makes sure that the algorithm can explore the search space efficiently.This potentially powerful optimization strategy can easily be extended to study multiobjective optimization applications with various constraints, even to NP-hard problems. Further studies can focus on the sensitivity and parameter studies and their possible relationships with the convergence rate of the algorithm.



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*Source: 453812-2013-03-17.xml*