# Particle Swarm Optimization with Power-Law Parameter Based on the Cross-Border Reset Mechanism.

I. INTRODUCTIONWith the rapid development of human society, people have to face much more complicated questions and have higher requirement for the optimal solution. Therefore, the outstanding optimization algorithms become more important. In the early 1990s, scholars proposed swarm intelligence optimization algorithms, which were inspired by the social activities mechanism of animals and insects in the nature [1]. Particle swarm optimization (PSO), which belongs to swarm intelligence optimization, has been widely used in particular. The mathematical operations of PSO are simple and pervasive because only a few parameters need to be adjusted. Moreover, it doesn't need higher requirement for the performance of CPU and RAM. The distributed parallel algorithms for PSO enhance the capability of processing large quantity of data and improve the execution speed. In recent years, there are many problems need to be solved about PSO. For instance, how to jump out of local optimum and improve search accuracy, how to reduce computational complexity and enhance convergence speed. These problems need to be solved especially when dealing with complex problems [2].

The algorithm can jump out of local optimum by balancing global and local search ability [3]. According to related researches, we learned the best search patterns for particular targets are the explosive, intermittent and occasional ones, which conform to the power-law distribution, rather than the distinct, systematic and regular ones [4]. Therefore, we introduce the principle of Levy flight to improve the traditional PSO, in which the value of parameters follow the power-law distribution and the pattern of particles movement conformed to Levy flight [5]. The particles make long distance movement in the search space with small probability and make short distance movement with large probability, which makes the particles can jump out of local optimum more easily. It provides new insights to solve the disadvantage of traditional PSO. This paper also proposed the cross-border reset mechanism to make the particles regain optimization ability when they stranded on the border of search space after a long distance movement. The proposed algorithms are compared with existing similar algorithms based on benchmark test functions and the handwriting character recognition system which developed by our group.

II. RELATED WORK

The swarm intelligence optimization has been increasingly used in the fields of engineering and economics. Scholars have proposed a series of bionic swarm intelligence algorithms, which typically include artificial ant colony algorithm, PSO, artificial fish algorithm, artificial bee colony algorithm, shuffled frog leaping algorithm, and firefly algorithm, etc [6-7]. Kar [8] showed that PSO is effective, easy to operate and can be widely used in many fields. However, PSO lack an effective mechanism to escape of local optimum and has other problems worthy to research [9]. In recent years, the researchers improve the performance of PSO by several methods, including adjusted the parameters [10], optimized the topology structure [11], used improved hybrid optimization algorithm and introduced biological mechanism [12]. This paper adjusts the parameters of algorithm by Levy flight dynamically and uses the cross-border reset mechanism to improve the performance of algorithm.

The values of parameters w, [c.sub.1], [c.sub.2] has an important role in the search process. The original PSO don't have inertia weight, which make the global search ability and local search ability unbalanced. Shi et al. [13] introduced the concept of inertia weight into the original PSO and proposed the standard particle swarm optimization (SPSO) in 1998. To make up for the shortcoming of linear decreasing inertia weight, Shi et al. [14] put forward fuzzy rules to adjust inertia weight according to the features of test functions. Clerc [15] proposed a random adjustment for inertia weight and Chatterjee [16] put forward a way of nonlinear inertia variation for dynamic adjustment in PSO. About the settings of [c.sub.1] and [c.sub.2], the traditional PSO uses fixed learning factors to achieve the best balance between global search and local search ability. For different problems, the ranges of [c.sub.1] and [c.sub.2] are both 1.0 to 2.5 in general. Suganthan [17] considered the best values of [c.sub.1] and [c.sub.2] are invariable. However, Ratnaweera et al. [18] used a linear function to adjust the learning factors, which make [c.sub.1] lessen and [c.sub.2] largen gradually and Zhang put forward a self-adjusting strategy of [c.sub.1] and [c.sub.2] based on the fitness values of the particles.

To improve the performance of algorithm, researchers begin to introduce biological mechanism into PSO and related algorithms appeared constantly, which have been proved effective in practical applications. Liu [19] used the flight mechanism of geese migration to improve the PSO's performance. Inspired by the symbiotic coevolution between species in nature, Chen [20] proposed a multi-species PSO, which extends the dynamics of the canonical PSO by taking into account species extinction and speciation events. Qin [21] was inspired by biological parasitic behavior and proposed a two species PSO, which refers to facultative parasitic behavior between hosts and parasites. Yang [22] proposed a new PSO based on the operator of chemotaxis in the bacterial foraging, which is easy to search for the optimal value in region.

In 1996, Viswanathan et al. [23] proposed the biological mechanism named as Levy flight by establishing the link between animal foraging behavior and random walk theory for the first time. They used GPS to research albatross foraging behavior and found the flight ranges of albatross following the power-law distribution, which consist with the foresight of Shlesinger a decade ago [24]. Levy flight is speculated the most effective foraging pattern when the foods are scattered over a large area. Nowadays, Levy flight is widely used to the optimization algorithm, which makes search efficiency maximization under uncertain environment. Literature [25] introduced the principle of Levy flight into the PSO and proposed the several improved PSO which based on Levy flight, including the algorithm's step transfer obey the power-law distribution (Levy Bare Bones), the algorithm which based on the hyperspheres (Levy Pivot) and the part of parameters obey the power-law distribution (Levy PSO). It's worth noting that Levy PSO will be abbreviated as LPSO in this paper. These several algorithms lay a solid foundation to the further improvement about PSO which based on the Levy flight and provide new insights into the improvement of PSO. Wang [26] proposed an earthquake disaster emergency rescue model based on cooperation mechanism and Levy flight to reduce the blindness and randomness of the rescue work. Li [27] proposed a variant of cooperative quantum-behaved PSO with two mechanisms to reduce the search space and avoid the stagnation, which are dynamic varying search area and Levy flight mechanism. Yan [28] put forward an improved bacterial foraging optimization algorithm based on Levy flight and Xie proposed an improved bat algorithm based on Levy flights and differential operators. In 2014, Hakli [29] proposed a novel particle swarm optimization algorithm with Levy flight (LFPSO). In the proposed method, a limit value is defined for each particle, and if the particles could not improve self-solutions at the end of current iteration, the limit is increased. If the limit value determined is exceeded by a particle, the particle is redistributed in the search space with Levy flight method. Experimental results show that the LFPSO is more successful than well-known and recent population-based optimization methods.

In this paper, we introduce the power-law distribution into the dynamic variation of parameters (w, [c.sub.1], [c.sub.2]) and make the step transfer follow the power-law distribution to enhance the ability of particles to jump out the local optimum. Furthermore, the coefficient of Levy flight was no longer use the experience values which rely on the large number of according to the problems appeared in the experiments, we propose the cross-border reset mechanism to improve the convergence accuracy of the algorithm and enhance the ability of particle to jump out of local optimum. Finally, the performance and accuracy of the proposed algorithms will be examined on well-known benchmark functions, comparing with SPSO and LFPSO as well. Furthermore, we have found researchers always test the performance of algorithms by practical application in the further step, Castillo O, et al. [30] test the performance of improved ant colony optimization by fuzzy control of a mobile robot, Martin D, et al. [31] test the performance of multi-objective genetic algorithm by a complex electromechanical process, Harmanani H M, et al. [32] test the performance of improved genetic algorithm by open-shop scheduling problem and Precup R E, et al. [33] test the nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models by anti-lock braking systems. In this paper, we will apply the proposed algorithms into handwriting character recognition system which developed by our group to test the performance of these algorithms as well.

III. THE PSO BASED ON THE LEVY FLIGHT AND CROSSBORDER RESET MECHANISM

This paper adjusts the value of parameters (w, [c.sub.1], [c.sub.2]) follow the power-law, which makes the pattern of particles transition conform to Levy flight. During the simulation, we found when a long distance movement happened, the particles have high possibility to strand on the border of search space and lost optimization ability. To solve this problem, we design the cross-border reset mechanism and propose particle swarm optimization with power-law parameter based on the cross-border reset mechanism (PLP-PSO-CBR). Referring to the framework of LFPSO [29], we embed the cross-border reset mechanism into LFPSO and proposed LFPSO-CBR. The two improved algorithms which based on cross-border reset mechanism will be described in this chapter and the performance will be analyzed in later chapters.

3.1 The Basic Principle of PSO

The principle of PSO can be described as follow. It is assumed that the search space is M-dimensional and the number of particles is N. The position of the ith particle at time t is expressed as [X.sub.1],(t) = ([x.sup.l.sub.i](t), [x.sup.2.sub.i](t), ...,[x.sup.M.sub.i](t)), of which the historical optimal position is expressed as [P.sub.besti] = ([P.sup.l.sub.i], [P.sup.2.sub.i], ..., [P.sup.M.sub.i]) according to the fitness of the ith particle. The optimal value among [mathematical expression not reproducible] is recorded as [G.sub.best] = ([G.sup.1], [G.sup.2], ..., [G.sup.M]) according to the fitness of all particles. The velocity of particles transition at time t+1 is defined as [V.sub.i](t+1) = ([v.sup.1.sub.i](t+1), [v.sup.2.sub.i](t+1), ... [v.sup.M.sub.i](t+1)) and the position of the ith particle at time t+1 is replaced according to the following equations.

[V.sup.d.sub.i](t + 1) = w x [v.sup.d.sub.i](t) + [c.sub.1] x rand x ([p.sup.d.sub.i] - [x.sup.d.sub.i](t)) + [c.sub.2] x rand x ([G.sup.d] - [x.sup.d.sub.i](t)), 1 [less than or equal to] i [less than or equal to] N,1 [less than or equal to] d [less than or equal to] M (1)

[x.sup.d.sub.i](t + 1) = [x.sup.d.sub.i](t) + [v.sup.d.sub.i] (t + 1), 1 [less than or equal to] i [less than or equal to] N,1 [less than or equal to] d [less than or equal to] M (2)

In Eq. (1), the constant [c.sub.1] and [c.sub.2] are learning factors, w is inertia weight, and rand is a random number between 0 and 1. The ranges of position and velocity in the dth dimension are [-[x.sup.d.sub.max], [x.sup.d.sub.max]] and [-[v.sup.d.sub.max], [v.sup.d.sub.max]]. PSO wi11 set the position as the boundary value when the particle stranded on the border of the dth dimension. The initial position and velocity of particle swarm are generated randomly, and they will be updated according to Eq. (1) and Eq. (2) until the stop condition is satisfied.

3.2 The Principle of Levy flight

The search pattern of Levy flight is different from the ordinary pattern because of the randomness. Levy flight is a type of random walk, of which the step sizes obey powerlaw distribution and the search directions obey uniform distribution. The proposed algorithms use the generator of Levy values with Mantegna rule [34]. In Mantegna rule, the step size is designed as follow:

s ([beta]) = u/[[absolute value of v].sup.1/[beta]] (3)

In Eq. (3), u and v obey normal distribution, u ~ N(0,[[sigma].sup.2.sub.u]), v ~ N(0,[[sigma].sup.2.sub.v]) x [[sigma].sub.u] and [[sigma].sub.v] are defined as follows:

[[sigma].sub.u] = 1 (4)

[[sigma].sub.v] = [{[GAMMA](1 + [beta])sin ([pi][beta]/2)/[GAMMA][(1 + [beta])/2][beta][2.sup.([beta] -1)/2]}.sup.1/[beta]] (5)

In Eq. (5), r is standard Gamma function, [GAMMA](n) = (n -1)!. In this paper, [beta] is coefficient of Levy flight and no constant value is taken for the [beta] parameter, but a random value in the (0, 2] interval is taken for each new distribution procedure [29]. If the [beta] value randomly so taken takes small values, it allow the particle perform very long jumps in the search space and prevents constantly being trapped in local minima, if big values are attained, it allow the particle perform short movement in the search space and continues to derive new values around the global optimal. The randomization can be more efficient as the steps obey a Levy distribution which can be approximated by the power-law. Therefore, the steps consist of many small steps and occasionally large-step or long-distance jump. Thus, Eq. (2) can be restated as follow:

[x.sup.d.sub.i](t + 1) = [x.sup.d.sub.i] (t) + [alpha] [direct sum] levy ([beta]) (6)

In Eq. (6), [alpha] generally is a random number and [direct sum] is dot product means entry-wise multiplications. In this paper, [alpha] is the step size which should be related to the scales of the problem of interest, [alpha] is random number for all dimensions of particles as well. Furthermore, Levy([beta]) can be calculated by Mantegna rule [35] as follow:

levy([beta])~0.01 u/[[absolute value of v].sup.1/[beta]], (0 < [beta] [less than or equal to] 2) (7)

In this paper, the parameters (w, [c.sub.1], [c.sub.2]) of PSO are generated according to Mantegna rule and obey the power-law distribution. Therefore, the velocity of particles transition is redefined as follow:

[mathematical expression not reproducible] (8)

In Eq. (8), [levy.sub.w]([beta]), [levy.sub.cl]([beta]) and [levy.sub.c2]([beta]) are produced respectively according to Eq. (3). In the meantime, Eq. (2) can be redefined as follow:

[x.sup.d.sub.i](t + 1) = [x.sup.d.sub.i](t) + [LV.sup.d.sub.i] (t + 1), 1[less than or equal to] i [less than or equal to] N,1 [less than or equal to] d [less than or equal to] M (9)

3.3 PLP-PSO-CBR

When we make the parameters (w, [c.sub.1], [c.sub.2]) follow the distribution of power-law, it means the pattern of particles transition will conform to Levy flight. During the experiments, we found the particles will strand on the border of search space occasionally after a long distance movement, which are unable to search further. To solve this problem, we introduce the cross-border reset mechanism and propose the PLP-PSO-CBR. The mechanism will initialize these particles which stranded on the border in the search space and make the initialized particles regain the optimization ability. The flow chart of PLP-PSO-CBR is shown in Figure 1. In the meantime, we can view the Pseudocode of PLP-PSO-CBR in Table I.

3.4 LFPSO-CBR

In the literature [29], the trial value and limit value are set for each particle. If the trial value is less than the limit value, the particles will move randomly in a small area. Otherwise, the particles make Levy flight in a large range. Thus, the transition model of the particles can switch between random walk with large probability and Levy flight with small probability. We also found inappropriate setting of limit value will disturb the balance of global and local search by extensive use of Levy flight step transfer mode. Referring to the framework of algorithm in literature [29], the LFPSOCBR is proposed based on the cross-border reset mechanism and aim to prove the validity of this mechanism. The flow chart of LFPSO-CBR is shown in Figure 2.

IV. THE EXPERIMENTAL DESIGN

The experiment is to verify the effectiveness of cross-border reset mechanism and the principle of Levy flight. We want to prove the algorithms can make the particles jumps out of the local optimum more easily and enhance the search accuracy when we introduce these two methods. LFPSO has shown the superior performance when compared with CLPSO, HPSO-TVAC, FIPSO, SPS0-40, DMS-PSO and other popular swarm intelligence algorithms in recent years, which include group search algorithm (GSO), cuckoo algorithm (CS) and firefly algorithm (FA) [33]. In this paper, the proposed algorithms are compared with LFPSO and SPSO for different

types of benchmark test functions. In addition, we will analyze the setting of parameters for the proposed algorithm.

There are 18 kinds of benchmark test functions in this experiment, which are mainly derived from the literatures [29], [36-37]. The benchmark test functions can be divided into three types, including unimodal function (U), normal multimodal function (M) and rotated multimodal function (R). The hardware environment for the experiments is Intel(R) Pentium(R) CPU G620 @ 2.60GHz, memory 8.00GB. The software environment is Windows 10 and MATLAB 2012a. The parameter setting about proposed algorithms are referenced in the literature [29], and the specific information is shown in Table II. The benchmark test functions are shown in Table III.

In addition, the algorithms can be freely used via http://123.57.158.232:88/PS0/ and we will update and maintain this website regularly. Researchers can validate and improve these algorithms by this website.

V. RESULTS AND ANALYSIS

In this paper, the algorithms based on the cross-border reset mechanism and Levy flight are compared with SPSO, LFPSO and LevyPSO (LPSO) to verify the search precision and the ability to jump out of the local optimum. The results are analyzed by MATLAB curve fitting toolbox in the further step. Furthermore, we provide setting of parameters by these results to improve the efficiency of the algorithms.

5.1 The Principle of Experiments

In the experiments, we have got the search space and optimum value about these benchmark test functions previously. The algorithm will find the optimum value in the search space based on different benchmark test functions and the result will getting closer to the optimum value according to fitness function during the optimization. In the experiments, the fitness function is the benchmark test function itself. Fox example, if we choose a benchmark test function to test the performance of algorithm, we need to get the lower-bound and upper-bound of this benchmark test function first and use them to construct the search space. Then, the algorithm will find the optimum value in the search space according to the fitness function. Finally, we can get the optimal solution, error value and convergence of this algorithm. However, the initialization of PSO is stochastic, we need to repeat the algorithm and get the mean value of results to reduce the error of randomness. In addition, the algorithms which include SPSO, LPSO, LFPSO, LFPSO-CBR and PLP-PSO-CBR can be freely used via http://123.57.158.232:88/PS0/ respectively. In the meantime, the benchmark test functions which are mentioned above can be freely used via http://123.57.158.232:88/PSO/5.SPSO/benchmark func.m and http://123.57.158.232:88/PSO/5.SPSO/func.m. Furthermore, the optimum value and search space of each function can be freely used via http://123.57.158.232:88/PSO/5.SPSO/get fun info.m.

5.2 Results of Contrast Experiments

In this work, we compare the proposed algorithms which based on the cross-border reset mechanism and Levy flight with SPSO, LFPSO, LevyPSO (LPSO) for 18 kinds of benchmark test functions and Table IV shows the simulation result. The simulation results contain the mean error and standard deviation error. The rank of algorithm depends on its mean error first and standard deviation error second. When the mean error between algorithms is close to each other, the algorithm with lower standard deviation error has a higher rank. The algorithms are evaluated between level 1 to level 5. The performance of algorithm is judged by the mean rank for the 18 benchmark test functions and we can compare the algorithms by the final rank.

In Table IV, some results are bold. It means the best result for benchmark test functions. The final rank indicate PLPPSO-CBR has the best performance, LFPSO ranked second, LFPSO-CBR ranked third, LPSO ranked fourth and SPSO is the worst. The results are analyzed in detail as follow.

The results between SPSO and LFPSO show that LFPSO has better performance for different types of benchmark test functions, which owe to the transition mode between random walk and Levy flight. The results also show that several improved PSO which based on the principle of Levy flight are better than SPSO, because the power-law distribution makes the pattern of particles transfer conform to the Levy flight and enhance the ability to jump out of local optimum.

During the experiments of LPSO, we found the particles always stranded on the border of search space after a long distance movement because the particles has lost the optimization ability. However, the cross-border reset mechanism can reset the stranded particles in the search space and make the initialized particles regain optimization ability. Therefore, PLP-PSO-CBR and LFPSO-CBR have better performance than LPSO.

By compare the simulation results between LFPSO and LFPSO-CBR. It can be found the performance of LFPSOCBR is better than LFPSO. The reason is inappropriate setting of limit value disturbs the balance of global search and local search by extensive use of Levy flight step transfer mode to different type of benchmark test functions in LFPSO. When we embedded the cross-border reset mechanism into LFPSO, it will help particles which stranded on the border regain the optimization ability. Thus, the performance of algorithm can improve by this method.

The simulation results between PLP-PSO-CBR and LFPSO-CBR both present an excellent performance in the experiments. When compared these two algorithms, we can found PLP-PSO-CBR shows better performance for most of benchmark test functions than LFPSO-CBR, because the limit value is difficult to determine in advance and the setting of limit value in LFPSO-CBR always by people's experience when dealing with different benchmark test functions. Although LFPSO can show a superior performance in several benchmark test functions, the setting of limit value will restricts the universality of the algorithm. Instead, PLP-PSO-CBR make the Levy flight model integrated into dynamic variation of parameters (w, [c.sub.1], [c.sub.2]). Researchers don't need large number of experiment results to determine the value of parameters and it will enhance the randomness of the algorithm. Therefore, PLP-PSO-CBR can show a strong universality and excellent performance for different type of benchmark test functions.

5.2 Analysis of Experiment Results

According the above analysis, PLP-PSO-CBR shows the best performance among the proposed algorithms. In this section, the relationships between error values, convergence point and iteration number for PLP-PSO-CBR are analyzed by MATLAB curve fitting toolbox. The MATLAB curve fitting toolbox can provide the common fitting functions, including exponential function, fourier function, gaussian function, interpolant function, polynomial function, power function, etc. The most appropriate fitting function can be found by this toolbox. In addition, the setting of parameters for PLP-PSO-CBR is given in this chapter and these settings will benefit to the efficiency of the algorithm.

1) Correlation analysis of error value and iteration number

The correlation analysis of error value and number of iterations for 9 kinds of benchmark test functions for PLPPSO-CBR are shown in Figure 3. We use number of iterations as horizontal axis and error value as vertical axis. The curves in Figure 3 are fitted by MATLAB curve fitting toolbox and the related parameters of the fitted curves are shown in Table V.

The results in Figure 3 indicate that the error value show exponential decreasing trends with the increasing numbers of iterations based on different types of the benchmark test functions. It means PLP-PSO-CBR won't fall into local optimum until the iteration number reach to threshold. According to the fitted curves in Figure 3, it can be found the threshold is around 10000 by limiting the angulations of fitted curves greater than 179 degrees when the total iteration number is 50000. Therefore, it is not necessary to execute the algorithm until the final iteration. The iteration can be stopped after 10000 iterations and it can reduce the execution time of PLP-PSO-CBR greatly.

The best model of fitting function for the relation between error value and number of iterations is the power function f (x) = a * [x.sup.b], which is found by MATLAB curve fitting toolbox. Moreover, the coefficient of power function will change in a certain range for different types of benchmark test functions. The coefficient h ranges between -1.3 to -1.1 when the benchmark test function is unimodal function, the coefficient h ranges between -0.8 to -0.5 when the benchmark test function is multimodal function and the coefficient h ranges between -1.4 to -1.2 when the benchmark test function is composite function. These results demonstrate the convergence speed of the algorithm depends on the types of benchmark test functions. The more complex benchmark test function is, the slower it convergence speed.

2) Correlation analysis of convergence point and total iteration number

The correlation analysis of convergence point and total iteration number for 9 kinds of benchmark test functions for PLP-PSO-CBR are shown in Figure 4. We use total iteration number as horizontal axis and convergence point as vertical axis. If the different between error value of two adjacent iteration numbers less than 1.00e-03, the previous iteration number is recorded as the convergence point. The convergence points of PLP-PSO-CBR for different total iteration number are described in Figure 4. The curves in Figure 4 are fitted by use of MATLAB curve fitting toolbox and the related parameters of the fitted curves are shown in Table VI.

The results in Figure 4 indicate that the convergence point show positive correlation with the total iteration numbers. The convergence points become higher with the increase of the total iteration numbers. It means the excessive setting of total iteration numbers won't improve the search precision because error value shows exponential decline with iteration numbers. Thus, the total iteration number needs to be set in a reasonable range and the time complexity of the algorithm can be reduced to some extent.

The best model of fitting function for the relationship between convergence point and total iteration numbers is polynomial function g(x) = [p.sub.1] * x + [p.sub.2], which is found by MATLAB curve fitting toolbox. The coefficient of polynomial function fluctuates from 0.02 to 0.03 for different types of benchmark test functions, which indicates the relation between convergence point and total iteration number has less to do with the type of benchmark test functions. Moreover, the total iteration numbers for different type of benchmark test functions can be estimated according to the required precision.

3) The convergence of PLP-PSO-CBR

In this paper, the convergence of this algorithm was analyzed by four coefficients. We use the correlation of error value and iteration number, correlation of convergence point and total iteration number to analyze the convergence by different benchmark test functions. In Figure 3, we can find PLP-PSO-CBR presents good convergence based on different type of benchmark test functions, it can jump out of local optimal more easier and get global optimal regardless of the type of functions. In the meantime, we found the algorithm has already converged before the end of the iteration. Therefore, it is not necessary to execute the algorithm until the end of iteration and we can set the stop point according to the total number of iterations. It will reduce the execution time of algorithm greatly. In Figure 4, we can find the convergence point presents positive correlation with the total iteration numbers based on different types of the benchmark test functions. Thus, the total iteration number needs to be set in a reasonable range and the time complexity of the algorithm can be reduced to some extent. Based on these results, we not only can prove the algorithm has a strong convergence, but also get some empirical values to improve the performance of the algorithm.

VI. APPLICATION

In general, we always use convolutional neural network (CNN) to recognize the handwritten images/characters and adjust the weight of CNN by back-propagation neural network (BPN). In this paper, we train the handwriting character recognition system by MNIST database of handwritten digits, this dataset contains 60,000 training sample data and 10,000 test data. Each data size is 28pi * 28 pi. We can use CNN and BPN to realize the recognition function in this system. In CCN, the first layer is the convolution layer, the number of convolutions is 2, the size of the convolution kernel is 5pi * 5pi. The activation function is sigmoid. The second layer is the mean pooling layer and the pool size is 2pi * 2pi. The third layer is the fully connected layer and the connection size is 288pi * 10pi. The activation function is sigmoid. In BPN, we use the batch gradient descent method to test the data, the batch size is 100 and the learning rate is 0.5. Original system is shown in Figure 5.

In addition, the application can be freely used via http://123.57.158.232:88/PS0/Application. Training data and test data which involved in this paper can be freely used via http://123.57.158.232:88/PS0/Application/Data. Furthermore, train-x is the training data that contains 60,000 image data, train-y is the corresponding tag for the training data, test-x is the test data that contains 10,000 image data and test_y is the tag corresponding to the test data.

In this section, we have improved the prediction accuracy of handwriting character recognition system by PLP-PSOCBR and verified the performance of this algorithm by simulation results. Improved system is shown in Figure 6.

In machine learning, there are two indicators measure the performance of the system. One is the fitting ability, the

Advances in Electrical and Computer Engineering other is the generalization ability. The fitting and generalization ability are both important for the system. We need to strike a balance between them to ensure the efficient of the system. The hardware environment for the experiments is Intel(R) Pentium(R) CPU G620 @ 2.60GHz, memory 8.00GB. The software environment is Windows 10 and MATLAB 2012a. The parameters' setting about algorithms is shown in Table VII. The training number or batch number is the CNN training process which uses the batch gradient descent method.

On the one hands, we can evaluate the fitting ability of the algorithm by Figure 7 - Figure 11. When comparing the PLP-PSO-CBR with other algorithms, we can find 1) It has a fast convergence rate, which ensures the system can find the global optimal in a short time. 2) It can achieve higher convergent accuracy, which leads to higher prediction accuracy in handwriting character recognition system. 3) It has lower initial error, which proves PLP-PSO-CBR is more effective for initial parameter optimization of CNN.

On the other hands, we can evaluate the generalization ability of this system by Table VIII. We estimated the error of prediction in handwriting character recognition system by different algorithms and found the accuracy of prediction is significantly improved when we use PLP-PSO-CBR to optimize this system. In summary, we can use PLP-PSOCBR to perfect the handwriting character recognition system and improve the performance of this system significantly by PLP-PSO-CBR when compared with other algorithms.

VII. CONCLUSION

In this paper, we propose the improved PSO with powerlaw parameter based on the cross-border reset mechanism. In this work, the parameters of algorithm meet the powerlaw distribution and the pattern of particles transition conforms to the Levy flight. The cross-border reset mechanism is designed to make the particles regain optimization ability when they stranded on the border. Results demonstrate the method which embedded the crossborder reset mechanism into power-law PSO can enhance the ability to jump out of the local optimum, this method also able to improve the accuracy of algorithm as well. PLP-PSO-CBR presents the best performance among other similar algorithms based on different benchmark test functions. In order to demonstrate the superiority of PLP-PSO-CBR, we apply PLP-PSO-CBR into the handwriting character recognition system. According to the results, we can find the convergence speed of PLP-PSO-CBR is faster, the accuracy of PLP-PSO-CBR is higher, the initial error of system is lower and it can jump out of local optimal more easier when compared with other algorithms in this system.

In addition, we use the MATLAB curve fitting toolbox to analyze the parameters in this algorithm. According to the relationship between error value and iterations, it shows the performance of algorithm will be affected by benchmark test functions and the error value began to stabilization after a certain number of iterations. Therefore, we can set the termination of iterations manually and it can reduce the time-consuming of the algorithm to some extent. According to the relationship between convergence point and number of iterations, it shows that the convergence point will move backward by the increase number of iterations, this means we should set the number of iterations in a reasonable range. Furthermore, we found the correlation of convergence point and number of iterations has a weak relationship by different type of benchmark test functions and we can calculate the number of iterations to terminate the algorithm in advance. For solving the higher dimensional optimization problem, cooperative game theory can be introduced to reduce the dimensionality in the future work.

This work was supported in part by the National Natural Science Foundation of China (No. 31601545); the Fundamental Research Funds for the Central Universities (No.KJQN201732); The Agricultural Project for New Variety, New Technology and New Model of Jiangsu Province (No.SXGC[2014]309); Government Sponsored Project Under the Science and Technology Development Initiative : Research and Demonstration of Electronic Food Safety Traceability (No. 2015BAK36B05); Key Research and Development Program of Jiangsu Province, Research and Demonstration on Key Technologies of Food Safety Risk Early Warning and Traceability Integrated Service Platform (No. BE2016803).

Haoyun WANG and Yuhan FEI equally contributed to this work.

Digital Object Identifier 10.4316/AECE.2017.04008

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Haoyun WANG (1,2,3), Yuhan FEI (1), Yibai LI (1), Shougang REN (1,3), Jianhua CHE (1,3), Huanliang XU (1,2,3) *

(1) College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China

(2) Agricultural Engineering Postdoctoral Research Station, Nanjing Agricultural University, Nanjing 210031, China

(3) Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, Nanjing 210095, China wanghy@njau.edu.cn, 2016201004@njau.edu.cn, 2016114003@njau.edu.cn, rensg@njau.edu.cn chejianhua@njau.edu.cn, huanliangxu@njau.edu.cn

Caption: Figure 1. The flow chart of PLP-PSO-CBR

Caption: Figure 2. The flow chart of LFPSO-CBR

Caption: Figure 3. Correlation analysis of error value and number of iterations

Caption: Figure 4. Correlation analysis of convergence point and iteration numbers

Caption: Figure 5. Original system

Caption: Figure 6. Improved system

Caption: Figure 7. CNN vs PLP-PSO-CBR

Caption: Figure 8. SPSO vs LPSO-CBR

Caption: Figure 9. LPSO vs PLP-PSO-CBR.

Caption: Figure 10. LFPSO vs PLP-PSO-CBR.

Caption: Figure 11. LFPSO-CBR vs PLP-PSO-CBR.

TABLE I. PSEUDOCODE OF PLP-PSO-CBR Initialize the dimension of search space M, the number of particles N Initialize the current iteration t, the maximum iterations T Initialize the position of all particles [X.sub.i,i=1,2,3, ..., N](t) randomly Initialize the fitness values of all particles according to benchmark test function Set the values of individual optimum [mathematical expression not reproducible] n and global optimum [G.sub.best] While t<T do Start the generator of levy value to update w, [c.sub.1], [c.sub.2] using Eq. (3) For i=1:N For d=1:M Update the velocity [LV.sup.d.sub.i](t + 1) of the ith particle using Eq. (8) Update the position [x.sup.d.sub.i](t) of the ith particle using Eq. (9) Use the cross-border reset mechanism according to whether stranded on the border End for Evaluate the fitness value for new position [X.sub.t] (t) according to benchmark test functions If [X.sub.i](t) is better than [mathematical expression not reproducible] Set X (t) to be [mathematical expression not reproducible] End if If [X.sub.i](t) is better than [G.sub.best] Set [X.sub.i] (t) to be [G.sub.best] End if End for t = t + 1 End while TABLE II. PARAMETER SETTINGS FOR THE ALGORITHMS Algorithm SPSO LFPSO/ LPSO LFPSO- CBR Population 50 50 50 Dimension 30 30 30 Iteration 50000 50000 50000 Inertia weight 0.7213 linear None decreasing Max iter - iter/ Max iter Learning factor [c.sub.1]= [c.sub.1]= Experience [c.sub.2] = 1. [c.sub.2]=2 Value 1931 Limit value -- 5 -- Repetition 15 15 15 Algorithm PLP -PSO- CBR Population 50 Dimension 30 Iteration 50000 Inertia weight Power-law distribution [beta] [member of] (0,2] Learning factor Power-law distribution [beta] [member of] (0,2] Limit value -- Repetition 15 TABLE III. LIST OF BENCHMARK TEST FUNCTIONS No Name Type Formula 1 Sphere U [F.sub.1] = [N.summation over (i=l)][x.sup.2.sub.i]1 2 Step U [F.sub.2] = [N.summation over (i=l)][ ([[x.sub.i] + 0.5]).sup.2] 3 Rosenbrock U [F.sub.3] = [N.summation over (i=l)] [100[([x.sub.i+1] - [x.sup.2.sub.i]).sup.2] + [([x.sub.i] - 1).sup.2]] 4 Quartic U [F.sub.4] = [N.summation over (i=l)] [ix.sup.4.sub.i] 5 Shifted Sphere U [mathematical expression not reproducible] 6 Shifted U [mathematical expression not Schwefel's reproducible] Problem 1.2 7 Rastrigin M [F.sub.7] = [N.summation over (i=1)] [[x.sup.2.sub.i] - 10cos(2[pi][x.sub.i]) + 10] 8 Ackley M [mathematical expression not reproducible] 9 Griewank M [mathematical expression not reproducible] 10 Schwefel 2.26 M [mathematical expression not reproducible] 11 Alpine M [F.sub.11] = [N.summation over (i=l)] [absolute value of ([x.sub.i] x sin([x.sub.i]) + 0.1 x [x.sub.i])] 12 Levy M [mathematical expression not reproducible] 13 Rotated R [F.sub.13] = [N.summation over (i=l)] hyper-ellipsoid [([i.summation over (j=l)]x).sup.2] 14 Rotated Schwefel R [mathematical expression not reproducible] 15 Rotated R [mathematical expression not Rastrigin reproducible] 16 Rotated Ackley R [mathematical expression not reproducible] 17 Rotated R [mathematical expression not Grienwank reproducible] 18 Rotated R [mathematical expression not Rosenbrock reproducible] TABLE IV. COMPARISON RESULTS OF ALGORITHMS No. Error SPSO LFPSO LPSO LFPSO- PLP-PSO- CBR CBR Mean 3.61e+03 1.42e-01 4.28e-01 1.26e-01 0.93e-01 1 Std. 1.20e+03 2.63e-02 3.61e-01 6.58e-02 9.21e-02 Rank 5 3 4 2 1 Mean 3.83e+03 2.63e+00 5.57e+02 2.80e+00 1.62e+00 2 Std. 1.37e+03 3.48e+00 3.62e+02 4.33e+00 2.58e+00 Rank 5 2 4 3 1 Mean 1.35e+02 2.97e+01 3.95e+01 1.23e-02 2.74e+01 3 Std. 6.07e+01 5.48e-01 1.50e+01 1.03e-02 5.32e-01 Rank 5 3 4 1 2 Mean 9.35e-01 4.72e-03 3.36e-02 4.21e-03 2.16e-03 4 Std. 4.47e-01 2.83e-03 2.45e-02 3.58e-03 2.04e-03 Rank 5 3 4 2 1 Mean 2.03e+04 3.31e+01 5.23e+03 2.35e+01 2.90e+01 5 Std. 5.93e+03 2.91e+01 4.01e+03 1.60e+01 2.69e+01 Rank 5 3 4 1 2 Mean 3.86e+04 2.03e+03 1.37e+04 1.73e+03 1.21e+03 6 Std. 1.52e+04 5.33e+02 5.21e+03 5.08e+02 5.56e+02 Rank 5 3 4 2 1 Mean 1.26e+02 3.17e+01 1.38e+02 3.61e+01 2.11e+01 7 Std. 2.32e+01 2.83e+01 3.26e+01 1.73e+01 2.42e+01 Rank 4 2 5 3 1 Mean 1.25e+01 7.01e-01 0.54e+01 7.09e-01 3.95e-01 8 Std. 1.81e+00 7.23e-01 3.81e+00 8.14e-01 6.58e-01 Rank 5 2 4 3 1 Mean 3.05e+01 3.73e-01 5.37e-01 3.45e-01 1.27e-01 9 Std. 1.07e+01 7.02e-02 4.77e-01 1.81e-01 7.82e-02 Rank 5 3 4 2 1 Mean 1.91e+02 1.72e+02 1.90e+02 1.55e+02 0.54e+02 10 Std. 2.63e+01 6.21e+01 3.46e+01 6.66e+01 5.60e+01 Rank 5 3 4 2 1 Mean 1.40e+01 5.76e-01 3.17e+00 5.25e-01 2.04e-01 11 Std. 3.63e+00 5.47e-01 2.36e+00 7.49e-01 5.01e-01 Rank 5 3 4 2 1 Mean 1.30e+01 1.79e+00 8.97e+00 1.14e+00 1.63e+00 12 Std. 5.14e+00 1.53e+00 3.83e+00 1.85e+00 1.15e+00 Rank 5 3 4 1 2 Mean 1.00e+04 3.23e+00 4.71e+01 2.79e+00 1.76e-01 13 Std. 5.14e+03 1.73e+00 6.52e+01 1.98e+00 1.56e+00 Rank 5 2 4 3 1 Mean 8.03e+02 8.42e-02 8.21e+02 7.63e-02 8.22e-02 14 Std. 1.25e+02 1.68e+02 1.74e+02 1.98e+02 1.69e-01 Rank 4 3 5 1 2 Mean 5.12e+02 4.97e-02 5.42e+02 4.23e-02 4.95e-02 15 Std. 1.07e+02 8.37e+01 9.52e+01 8.12e+01 1.47e+00 Rank 4 3 5 1 2 Mean 6.91e+02 5.09e-01 5.52e+02 5.14e-01 4.96e-01 16 Std. 1.73e+02 1.03e+02 1.04e+02 8.37e+01 1.08e+00 Rank 5 2 4 3 1 Mean 1.08e+03 1.27e-03 1.14e+03 1.04e-03 1.00e-03 17 Std. 4.47e+01 6.82e+01 6.07e+01 8.03e+01 6.79e-03 Rank 4 3 5 2 1 18 Mean 1.07e+03 1.37e-02 0.74e+03 1.00e-03 1.03e-03 Std. 7.21e+01 5.26e-01 7.49e+01 6.91e-01 5.76e-02 Rank 5 3 4 1 2 Mean 4.78 2.56 4.22 1.94 1.33 Rank Final 5 3 4 2 1 Rank TABLE V. RELATED PARAMETERS OF THE FITTED CURVES Model of fitting function f (x) = a x [x.sup.b] Benchmark test Type Parameters of function function a b Sphere U 2.074e+07 -1.357 Rosenbrock U 2.081e+04 -1.134 Quartic function U 3.731e+03 -1.286 Ackley M 3.922e+02 -0.549 Alpine M 1.956e+03 -0.717 Levy M 7.462e+03 -0.835 Rotated hyper- R 2.154e+08 -1.484 ellipsoid Rotated Schwefel R 1.069e+08 -1.348 Rotated Rastrigin R 1.009e+07 -1.237 TABLE VI. RELATED PARAMETERS OF THE FITTED CURVES Model of fitting function g(x) = [p.sub.1] x x + [p.sub.2] Function Type Parameters of function p1 p2 Sphere U 0.02074 73.85 Rosenbrock U 0.02829 29.78 Quartic function U 0.03043 22.98 Ackley M 0.03414 -3.96 Alpine M 0.03266 16.73 Levy M 0.03329 0.85 Rotated hyper- R 0.02732 47.03 ellipsoid Rotated Schwefel R 0.02584 76.08 Rotated Rastrigin R 0.02553 83.31 TABLE VII. PARAMETER SETTINGS FOR THE ALGORITHMS Algorithm SPSO LFPSO/ LPSO LFPSO-CBR Population 50 50 50 Dimension 52 52 52 Iteration 500 500 500 Inertia 0.7213 linear None weight decreasing Max iter - iter/ Max. iter Learning [c.sub.1] = [c.sub.1] = Experience factor [c.sub.2] = [c.sub.2] = 2 Value 1.1931 Limit value -- 5 -- Repetition 10 10 10 Training 600 * 20 600 * 20 600 * 20 number Batch number 12000 12000 12000 Dataset 60000 60000 60000 Algorithm PLP-PSO- CBR Population 50 Dimension 52 Iteration 500 Inertia Power-law weight distribution 0e (0,2] Learning Power-law factor distribution [beta] [member of] (0,2] Limit value -- Repetition 10 Training 600 * 20 number Batch number 12000 Dataset 60000 TABLE VIII. RELATED PARAMETERS OF THE FITTED CURVES Algorithms Error of Prediction Rank PLP-PSO-CBR 4.16% 1 LFPSO-CBR 5.92% 3 LFPSO 6.62% 4 LPSO 7.36% 5 SPSO 8.83% 6 CNN 5.21% 2

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Author: | Wang, Haoyun; Fei, Yuhan; Li, Yibai; Ren, Shougang; Che, Jianhua; Xu, Huanliang |
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Publication: | Advances in Electrical and Computer Engineering |

Article Type: | Report |

Date: | Nov 1, 2017 |

Words: | 8888 |

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