Honey Bees Inspired Learning Algorithm: Nature ...

Honey Bees Inspired Learning Algorithm: Nature Intelligence Can Predict Natural Disaster Habib Shah, Rozaida Ghazali and Yana Mazwin Mohmad Hassim Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia (UTHM) Parit Raja, 86400 Batu Pahat. Johor, Malaysia

[email protected], [email protected], [email protected]

Abstract. Artificial bee colony (ABC) algorithm which used the honey bee intelligence behaviors, is a new learning technique comparatively attractive for solving optimization problems. Artificial Neural Network (ANN) trained with the ABC algorithm normally has poor exploration and exploitation processes due to the random and similar strategies for finding best position of foods. Global artificial bee colony (Global ABC) and Guided artificial bee colony (Guided ABC) algorithms used to produce enough exploitation and exploration strategies respectively. Here, a hybrid of Global ABC and Guided ABC is proposed called Global Guided ABC (GG-ABC) algorithm, for getting balance and robust exploitation and exploration process. The experimental result shows that the GG-ABC performed better than other algorithms for prediction of earthquake hazards. Keywords: Guided Artificial Bee Colony, Global Artificial Bee Colony, Global Guided Artificial Bee Colony algorithm, Earthquake prediction.

1

Introduction

In the past decade, the fracture of earth, flow of rocks, movements of tectonic plates, heat waves temperature and the high range of sea waves has been focused by geologists and engineers for saving human life as well as economic of the region. These sources may be the most important rule in earthquake, water level height and tsunami occurrence called seismic signals or natural hazards. Seismic events, especially earthquake is the most costly natural hazards faced by the nation in which they occur without prior warning and may cause severe injuries [1]. The intensity of occurrence of such event creates disasters and can change human and animals lives [2]. In recent years much has been learned from natural disasters and risk to infrastructure systems. In 2011, natural disaster costs (US$ 365.6 billion) were the highest of the decade, accounting for almost 1.5 times the direct losses reported in 2005 (US$ 248 billion, 2011 prices) [3]. The countless lives in earthquake risk areas can be saved, and the human and economic losses caused by these events can be reduced [3].

Seismic time series signals prediction are the challenges for researchers which can be solved through proper ANN model and robust learning algorithms [4]. The robust learning algorithm has the properties like high prediction accuracy and very less error. This can be achieved through optimal weight values, accepted NN model, getting the behaviours of data, careful selection of learning parameters, data pre-processing methods, suitable number of layers, nodes and appropriate activation function [5].Traditional learning algorithms of ANN like Backpropagation (BP) is well-known for getting the solution in different applications with various tasks; however they easily got struck in local minima. The swarm intelligence (SI) algorithms used to train ANN for finding best parameter values. Besides solving other optimization problems through ABC, getting the optimal weight values for ANN through training procedures based artificial behaviours of bees getting best results made it more famous. All types of SI algorithms are successfully applied to statistical and engineering tasks, and showed the best performance from others local search algorithms [6]. SI learning algorithms with optimal exploration and exploitation procedures provide high performance for a given task. In this work, the GG-ABC algorithm is proposed for getting high accuracy for natural disaster prediction, like earthquake magnitude using natural inspiration techniques of honey bees. The performance of GG-ABC is compared with standard ABC, Guided ABC and Global ABC algorithms. The GG-ABC algorithm is used successfully to train Multilayer Perceptron (MLP) for earthquake magnitude prediction.

2

Honey Bees Inspired Learning Algorithms

Bio inspired intelligence learning techniques deals with natural and artificial systems that composed of many individuals agents, with coordinated patterns using decentralized control, adaptively, unity, cooperation and self organization properties. These are evolutionary algorithms (EAs), particle swarm optimization (PSO), ant colony (ACO), cuckoo search (CS), artificial bee colony (ABC) and so on, can be used for solving optimization problems [7]. Bio inspired techniques focuses on the collective behaviours that result from the local interactions of the individuals with each other and with their environment, through the artificial gathering of ants, fish, birds, herds of land animals, and honey bees. These algorithms are relatively new optimization techniques which have been shown to be competitive to other non-swarm algorithms. These natural intelligence techniques are popular for exploration and exploitation process, where robust swarms agents provide strong amount exploration and exploitation with balance quantity [8]. The bees intelligence algorithms well-known with their nature beauty, robust performance, global and neighbour acceptable knowledge. Social insect colonies can be considered as dynamic system of gathering information from the environment and adjusting its behaviour in accordance to it. Generally, all social insect colonies behave according to their own division of labours related to their morphology. Bee system, consists of two essential components. These components are foods and foragers, where the amount of food source depends on various parameters such as its closeness to the nest, richness of energy and ease of extracting this energy [9]. There are three types bees working as a team namely: employed and onlooker are exploiters,

while scout function is to explore. Fig 1 shows the behavior of honey bee foraging for nectar.

Fig. 1. Minimal Model of Foraging Behaviour of Honey Bees.

where; UF: Unemployed Foragers, S: Scout, EF1:Employed Forager, EF2: Employed Forager, U: Undiscovered Food Sources R: Recruited Forager, SN: Food Sources. Mathematically, collecting exploration and exploitation respectively through standard ABC can be written as:

vij  xij  ij xij  xkj 

(1)



xijrand  xijmin  rand 0,1 xijmax  xijmin



(2)

where, k is a solution in the neighborhood of i, φ is a random number. The standard ABC has been extended by researchers to many different versions such as gbest, guided best, so best so far and hybridization with local for various tasks [8]. 2.1

Global Artificial Bee Colony (Global ABC) Algorithm

The most favorable solution for optimization problems are through exploration and exploitation which should be balanced with effective quantity for each individual agents of swarm based learning algorithms like ABC and PSO. To increase exploitation step within specified area, Global ABC algorithm [10] which is an optimization tool provides stochastic search procedure in which agents are adapted by the global best artificial bees with time, and the bee’s aim is to discover the best places of global food sources [7]. This gbest strategy has good exploitation amount in the employed and onlooker bees sections. Unfortunately, the Global ABC cannot provide the strong exploration, because this technique guides the employed and onlookers agents only, so that exploration procedure cannot be improved. The gbest search strategy used in employed and onlooker are given in Eq (3):







vij  xij  ij xij  xkj   c1rand 0,1 x best  xij  c2 rand 0,1 y best  xij j j



(3)

where y shows best food source, C1 and C2 are two positive constant values, xbestJ is the jth element of the global best solution found so far, ybestJ is the jth element of the best solution in the current iteration. 2.2

Guided Artificial Bee Colony (Guided ABC) Algorithm

The Guided ABC algorithm is an advanced version of standard ABC, which proposed for improving the exploration procedure through scout bee searching strategy [11]. Guided ABC has heuristic procedure for the solution of combinatorial optimizations and discrete problems that has inspired by honey bees. The Guided ABC is an attractive algorithm because it use the real bee agent for solving the optimization. In the Guided ABC algorithm the scout bee has been guided to get optimal food source position instead of random methods, which used to increase the exploration process for given task. The scout will generate a new solution through global best knowledge information. The global best experience will modify with the following best guided strategy as:

vij  xij   * xij  xkj   ( 1   )* ( xij  xbest j )

(4)

The Guided ABC will increase the capabilities of the standard ABC to produce new best solutions located near the feasible area. This technique is successfully used for constrained optimization problems with enough exploration [11], however the employed and onlookers bees have random strategy with poor exploitation.

3

The Proposed Global Guided Artificial Bee Colony Algorithm

Natural intelligence algorithms become attractive due to their robust searching ability through the neighbour agent information. Habitually the exploration and exploitation are the strategies which can be improved with the best movement of neighbour agent information in standard bees algorithm. The performance of ABC algorithm depends on agent dancing and strong power of intelligence, so if the agent has enough intelligence, it can provide strong exploration and exploitation process in the particular area for given problems. In bee algorithm, the employed and onlookers bees have the duty of exploitation procedures. While, the scout bees are used for getting enough amount of exploration. The Global ABC has successfully improved the exploitation through global best bees methods. Furthermore the guided ABC has outstanding performance with strong exploration due to the guided scout bees. Combining the gbest agent strategies of Global ABC and Guided ABC algorithms for getting enough amount of exploration and exploitation with balance quantity. The new hybrid algorithm called Global Guided Artificial Bee Colony (GG-ABC) algorithm. The GG-ABC agents used global employed / onlookers and guided scout bees are used to find the best food source. The GG-ABC will merge their best finding approaches with original ABC by the following steps. The exploitation procedure will

increase through Eq 5 using global ABC strategy, and exploration with Eq 6, through guided scout bees. The pseudo code of the proposed GG-ABC algorithm detailed as: Initialize the food source positions REPEAT Global Employed Bee Stage Global Onlooker Bee Stage Guided Scout Bee Stage Memorize the best solution achieved until now Until Maximum Cycle Number (MCN). Mathematically the exploitation and exploration process can be improved and balance with enough amount through the following equations. Global Employed / Onlooker Bee Phase







vij  xij  ij xij  xkj   c1rand 0,1 x best  xij  c2 rand 0,1 y best  xij j j



(5)

Guided Scout Bee Phase

vij  xij   * xij  xkj   ( 1   )* ( xij  xbest j )

(6)

Where vij shows best food source, c1 and c2 are two constant values which is 2.5 and 1.5 for this study respectively, xbest j is the jth element of the global best solution found so far, yjbest is the jth element of the best solution in the current iteration, øij is a uniformly distributed real random number in the range [-1, 1].

4

Experimental Design

In this research, the natural disaster earthquake significant parameter called magnitude measured through the Richter scale is used for prediction. The univariate time series data was taken from the Southern California Earthquake Data Centre (SCEDC) holdings for November and December 2013 (SCEDC, 2013) and Northern California Earthquake Data Centre (NCEDC) for November and December of 2012. Seismic time series data are highly non-linear and non-stationary signals which exhibit high volatility, complexity and noise. Therefore, seismic time series need preprocessing before presenting them to MLP for training and testing. In this work, GG-ABC algorithm is used to train MLP for the South and Northern California earthquake time series data for prediction tasks. To calculate the performance of the ABC, Global ABC, Guided ABC and GG-ABC algorithms by Mean of Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and Signal to Noise Ratio (SNR). The stopping criteria for ABC and Global ABC, Guided ABC and GG-ABC stopped on 2000 MCN with 20 colony size.

5

Simulation Results and Analysis

The proposed GG-ABC algorithm was utilized to predict the occurrence of earthquake magnitude value within seconds of South and Northern California earthquake

dataset. These time series were fed to the MLP to capture the underlying rules of the earth movement in the specified regions of South and Northern California. For comparison, the evaluation of each learning algorithm used for the prediction tasks are summarized based on average results of 10 runs, which are further explained in the following subsections. 5.1

South California Earthquake Prediction.

The best average evaluation results using all learning algorithms for South California earthquake prediction are given in Tables 1 to 3 and Figures 2 to 4 respectively. In Table 1, the MSE for testing data set is presented. The proposed GG-ABC gives small error from other algorithms in terms of MSE and NMSE for the South California earthquake prediction task as given in Table 1 and 2. Table 1. Average MSE on out of sample data for South California earthquake prediction NN Structure

ABC

Global ABC

Guided ABC

GG-ABC

5-2-1 5-3-1 5-5-1 5-7-1 5-9-1

0.000723 0.000521 0.000501 0.000442 0.000431

0.0004912 0.0004201 0.0003101 0.0002981 0.0001781

0.0005326 0.0003301 0.0003110 0.0002089 0.0001162

0.0001326 0.0001021 0.0001007 0.0000531 0.0000330

Meanwhile the maximum SNR values for out of sample data from average simulation results are given in Table 3. The maximum SNR on unseen data reached to 36.9521 value by the proposed GG-ABC, which is better than other learning algorithms for South California earthquake prediction. Throughout the training and testing process of GG-ABC algorithm. Table 2. Average NMSE on out of sample data for South California earthquake prediction NN Structure

ABC

Global ABC

Guided ABC

GG-ABC

5-2-1 5-3-1 5-5-1 5-7-1 5-9-1

0.161091 0.111025 0.101034 0.089511 0.076101

0.141021 0.101530 0.101191 0.094154 0.052101

0.246391 0.206952 0.124639 0.102411 0.028345

0.116301 0.099844 0.054652 0.022942 0.010139

Table 3. Average SNR on out of sample data for South California earthquake prediction NN Structure

ABC

Global ABC

Guided ABC

GG-ABC

5-2-1 5-3-1 5-5-1 5-7-1 5-9-1

24.4815 24.0012 25.1093 25.0061 26.4211

26.0012 27.0234 28.1981 28.0283 30.4299

25.0311 26.0209 28.1961 29.0103 30.0911

29.2982 30.8873 33.1209 32.1892 36.9521

Fig 2 represents learning curves for the proposed GG-ABC algorithms applied to a MLP. For each topology, the network was trained on successive input examples for

the number of iterations shown. From Fig 2, which is the convergence curve of proposed GG-ABC algorithm for South California earthquake magnitude prediction shows fast convergence on 200 Maximum Cycle Number (MCN). This is because of strong exploration and exploitation procedures through global and guided technique. The prediction results the out-of-sample data are reported in Fig 3 and 4. After completing several simulations for predicting earthquake magnitude based on the past historical data using GG-ABC algorithm, it is concluded that the average error for prediction of earthquake is smaller. For the presentation purpose, the first 100 samples of data has been selected from earthquake magnitude prediction. From Fig 3 and 4 show that the predicted earthquake signal's are close to the real values.

Fig. 2. Learning curves of South California earthquake using proposed GG-ABC algorithm

Fig. 3.: Prediction of South California earthquake on out of sample data by GG-ABC algorithm

Fig. 4.: Prediction of South California earthquake on out of sample data by GG-ABC algorithm

5.2

Northern California Earthquake Prediction.

The best average simulation results using all algorithms for Northern California earthquake prediction are given in the Tables 4 to 6 and Fig 5, 6 and 7. In Table 4, the MSE for testing data set is presented. The results for earthquake prediction from Tables 4 to 6 obviously demonstrated that the GG-ABC algorithm in all cases achieved very small error compared to other algorithms. Table 6 clearly shows that the highest SNR values were obtained by the proposed GG-ABC algorithm. Table 4.

Average MSE on out of sample data for Northern California earthquake prediction

NN Structure

ABC

Global ABC

Guided ABC

GG-ABC

5-2-1 5-3-1 5-5-1 5-7-1 5-9-1

0.000321 0.000392 0.000290 0.000359 0.000220

0.000273 0.000211 0.000202 0.000120 0.000147

0.000365 0.000391 0.000253 0.000223 0.000112

0.0001383 0.0003092 0.0001857 0.0001002 0.0000992

Table 5. Average NMSE on out of sample data for Northern California earthquake prediction

NN Structure

ABC

Global ABC

Guided ABC

GG-ABC

5-2-1 5-3-1 5-5-1 5-7-1 5-9-1

0.178905 0.128921 0.105611 0.079013 0.049625

0.110931 0.102078 0.097523 0.070923 0.042987

0.216598 0.127091 0.101102 0.092056 0.068732

0.110973 0.098752 0.025691 0.029786 0.011023

Table 7 contains the maximum SNR for Northern California earthquake magnitude prediction with average simulation results using the above learning algorithms. The maximum SNR for out of sample data obtained by Global ABC reached to 30.4986, which is less from other algorithms. The GG-ABC reached to 38.1218 value for SNR with Nine hidden nodes, which is better than all learning algorithms. So the proposed GG-ABC algorithm has the overall outstanding maximum SNR value. Table 6.

Average SNR on out of sample data for Northern California earthquake prediction

NN Structure

ABC

Global ABC

Guided ABC

GG-ABC

5-2-1 5-3-1 5-5-1 5-7-1 5-9-1

28.1201 29.9826 30.9542 30.4321 30.4986

29.1832 30.0714 30.1561 32.0139 33.4216

28.4572 30.1051 30.1002 31.9823 31.5627

34.2236 35.8875 36.4389 36.1702 38.1218

The table 6 shows that the performance of GG-ABC is better when compared with other learning algorithms. The proposed algorithm played quite an important role in a network's performance. Fig 5, obtained by GG-ABC algorithm in training step for earthquake seismic time-series, where the MSE is also stable and converged quickly.

Fig. 5. Learning curves of Northern California earthquake using proposed GG-ABC algorithm

The prediction of earthquake time-series for unseen data using GG-ABC is given in Fig 6 and 7 with a different number of hidden nodes. From these figures, the best average prediction results through GG-ABC indicate close to actual signal.

Fig. 6 : Northern California earthquake prediction on out of sample data by GG-ABC algorithm

Fig. 7: Northern California earthquake prediction on out of sample data by GG-ABC algorithm

Fig 6 and 7 shows the prediction performance of GG-ABC compared with the desired values as graphed for Northerly California earthquake prediction. The above simulation results demonstrate, that GG-ABC algorithm has successfully predicted the earthquake magnitude of South and Northern California with less error. The nonlinear dynamical behavior is induced by the bee nature intelligence. Therefore, it leads to the best input output mapping and an optimum prediction.

6

Conclusion

In this paper, GG-ABC algorithm is used to find the optimal weight set for MLP through bees. It has been proved that the GG-ABC algorithm is successfully used in improving bees intelligence which increase the exploration and exploitation process. The results show that the GG-ABC algorithm possesses high performance in earthquake prediction than other algorithms. Hence, the GG-ABC algorithm may be a good alternative to deal with earthquake prediction. It is interesting that despite the high evolution in technology, simply nature can be used to predict natural disasters using an artificial intelligence. Acknowledgment The authors would like to thank University Tun Hussein Onn Malaysia and Ministry of High Education (MOHE) for supporting this research under the ERGS Vote 0882. REFERENCES 1. 2.

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