Habib Shah

Prediction of Earthquake Magnitude by an Improved ABC-MLP

Different algorithms have been used for training neural networks (NNs) such as back propagation (BP), gradient descent (GA), partial swarm optimization (PSO), and ant colony algorithm (ACO). Most of these algorithms focused on NNs weight values, activation functions, and network structures for providing optimal outputs. Ordinary BP is one well known technique which updates the weight values for minimizing error but still it has some drawbacks such as trapping in local minima and slow convergence. Therefore, in this work a population based algorithm called an Improved Artificial Bee Colony (IABC) algorithm is proposed for improving the training process of Multilayer Perceptron (MLP) in order to overcome these issues by optimal weight values. Population based algorithm makes MLP attractive because of the social insects training algorithm. It investigates the improved weights initialization technique using IABC-MLP. The performance of IABC-MLP is benchmarked against MLP train with the standard BP. The experimental result shows that IABC-MLP performance is better than BP-MLP for earthquake time series data.

Hybrid Ant Bee Colony Algorithm for Volcano Temperature Prediction

A social insect’s techniques become more focus by researchers because of its nature behavior processing and by training neural networks through agents. Chief among them are Swarm Intelligence (SI), Ant Colony Optimization (ACO), and recently Artificial Bee Colony algorithm, which produced easy way for solving combinatorial problems and for training NNs. These social based techniques mostly used for finding optimal weight values in NNs learning. Usually, NNs trained by a standard and well known algorithm called Backpropagation (BP) have difficulties such as trapping in local minima, slow convergence or might fail sometimes. For recovering the above cracks the population or social insects based algorithms used for training NNs for minimizing network output error. Here, the hybrid of nature behavior agents’ ant and bees combine’s techniques used for training ANNs. The simulation result of a hybrid algorithm compared with, ABC and BP training algorithms. From the experimental results, the proposed Hybrid Ant Bee Colony (HABC) algorithm did improve the classification accuracy for prediction of a volcano time-series data.

Global artificial bee colony algorithm for boolean function classification

This paper proposed Global Artificial Bee Colony algorithm for training Neural Network (NN), which is a globalised form of standard Artificial Bee Colony algorithm. NN trained with the standard backpropagation (BP) algorithm normally utilizes computationally intensive training algorithms. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome, GABC algorithm used in this work to train MLP learning for classification problem, the performance of GABC is benchmarked against MLP training with the typical BP, ABC and Particle swarm optimization for boolean function classification. The experimental result shows that MLP-GABC performs better than that standard BP, ABC and PSO for the classification task.

A survey on bug prioritization

Daily large number of bug reports are received in large open and close source bug tracking systems. Dealing with these reports manually utilizes time and resources which leads to delaying the resolution of important bugs. As an important process in software maintenance, bug triaging process carefully analyze these bug reports to determine, for example, whether the bugs are duplicate or unique, important or unimportant, and who will resolve them. Assigning bug reports based on their priority or importance may play an important role in enhancing the bug triaging process. The accurate and timely prioritization and hence resolution of these bug reports not only improves the quality of software maintenance task but also provides the basis to keep particular software alive. In the past decade, various studies have been conducted to prioritize bug reports using data mining techniques like classification, information retrieval and clustering that can overcome incorrect prioritization. Due to their popularity and importance, we survey the automated bug prioritization processes in a systematic way. In particular, this paper gives a small theoretical study for bug reports to motivate the necessity for work on bug prioritization. The existing work on bug prioritization and some possible problems in working with bug prioritization are summarized.

Global hybrid ant bee colony algorithm for training artificial neural networks

Learning problems for Neural Networks (NNs) has widely been explored from last two decades. Population based algorithms become more focus by researchers because of its nature behavior processing with optimal solution. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) Algorithms produced easy way for training NNs. These social based techniques mostly used for finding optimal weight values and over trapping local minima in NNs learning. Typically, NNs trained by a traditional and recognized algorithm called Backpropagation (BP) has difficulties such as trapping in local minima, slow convergence or might fail sometimes. In this research, the new method named Global Hybrid Ant Bee Colony (GHABC) algorithm used to train NNs to recover the BP gaps. The simulation result of a hybrid algorithm evaluates with ordinary ABC, Levenberg-Marquardt (LM) training algorithms. From the investigated results, the proposed GHABC algorithm did get better the learning efficiency for NNs using Boolean Function classification task.

Global Artificial Bee Colony-Levenberq-Marquardt GABC-LM Algorithm for Classification

The performance of Neural Networks NN depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insects behavior learning algorithms. Chief among these are, Ant Colony Optimzation ACO, Artificial Bee Colony ABC algorithm, Hybrid Ant Bee Colony HABC algorithm and Global Artificial Bee Colony Algorithm train Multilayer Perceptron MLP. This paper investigates the new hybrid technique called Global Artificial Bee Colony-Levenberq-Marquardt GABC-LM algorithm. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome GABC-LM algorithm used in this work to train MLP for the boolean function classification task, the performance of GABC-LM is benchmarked against MLP training with the typical LM, PSO, ABC and GABC. The experimental result shows that GABC-LM performs better than that standard BP, ABC, PSO and GABC for the classification task.

Soft Computing Approach in Modeling Energy Consumption

In this chapter, we build an intelligent model based on soft computing technologies to improve the prediction accuracy of Energy Consumption in Greece. The model is developed based on Genetic Algorithm and Co-Active Neuro Fuzzy Inference System (GACANFIS) for the prediction of Energy Consumption. For verification of the performance accuracy, the results of the propose GACANFIS model were compared with the performance of Backpropagation Neural network (BP-NN), Fuzzy Neural Network (FNN), and Co-Active Neuro Fuzzy Inference System (CANFIS). Performance analysis shows that the propose GACANFIS improve the prediction accuracy of Energy Consumption as well as CPU time. Comparison of the results with previous literature further proved the effectiveness of the proposed approach. The prediction of Energy Consumption is required for expanding capacity, strategy in Energy supply, investment in capital, analysis of revenue, and management of market research.

Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Many different earning algorithms used for getting high performance in mathematics and statistical tasks. Recently, an Artificial Bee Colony (ABC) developed by Karaboga is a nature inspired algorithm, which has been shown excellent performance with some standard algorithms. The hybridization and improvement strategy made ABC more attractive to researchers. The two famous improved algorithms are: Guided Artificial Bee Colony (GABC) and Gbest Guided Artificial Bee Colony (GGABC), are used the foraging behaviour of the gbest and guided honey bees for solving optimization tasks. In this paper, GABC and GGABC methods are hybrid and so-called Hybrid Guided Artificial Bee Colony (HGABC) algorithm for strong discovery and utilization processes. The experiment results tested with sets of numerical benchmark functions show that the proposed HGABC algorithm outperforms ABC, PSO, GABC and GGABC algorithms in most of the experiments.

Honey Bees Inspired Learning Algorithm: Nature Intelligence Can Predict Natural Disaster

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.

G-HABC Algorithm for Training Artificial Neural Networks

Learning problems for Neural Network (NN) has widely been explored in the past two decades. Researchers have focused more on population-based algorithms because of its natural behavior processing. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) algorithm produced an easy way for NN training. These social based techniques are mostly used for finding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has difficulties such as trapping in local minima and slow convergence. The new method named Global Hybrid Ant Bee Colony (G-HABC) algorithm which can overcome the gaps in BP is used to train the NN for Boolean Function classification task. The simulation results of the NN when trained with the proposed hybrid method were compared with that of Levenberg-Marquardt (LM) and ordinary ABC. From the results, the proposed G-HABC algorithm has shown to provide a better learning performance for NNs with reduced CPU time and higher success rates.