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Motivation

A major thrust in algorithmic development is the design of algorithmic models to solve increasingly complex problems. Enormous successes have been achieved through the modeling of biological and natural intelligence, resulting in so-called “intelligent systems”.

 


Research Works

The best-so-far selection in artificial bee colony algorithm

Anan Banharnsakun, Tiranee Achalakul, Booncharoen Sirinaovakul

Abstract

The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified method for solution update of the onlooker bees in this paper. In our method, the best feasible solutions found so far are shared globally among the entire population. Thus, the new candidate solutions are more likely to be close to the current best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, in each iteration, we adjust the radius of the search for new candidates using a larger radius earlier in the search process and then reduce the radius as the process comes closer to converging. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. We empirically assess the performance of our proposed method on two sets of problems: numerical benchmark functions and image registration applications. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm.



Read more | August 8, 2014

Job shop scheduling with the best-so-far ABC

Anan Banharnsakun, BooncharoenSirinaovakul, TiraneeAchalakul

Abstract

The Job Shop Scheduling Problem (JSSP) is known as one of the most difficult scheduling problems. It is an important practical problem in the fields of production management and combinatorial optimization. Since JSSP is NP-complete, meaning that the selection of the best scheduling solution is not polynomially bounded, heuristic approaches are often considered. Inspired by the decision making capability of bee swarms in the nature, this paper proposes an effective scheduling method based on Best-so-far Artificial Bee Colony (Best-so-far ABC) for solving the JSSP. In this method, we bias the solution direction toward the Best-so-far solution rather a neighboring solution as proposed in the original ABC method. We also use the set theory to describe the mapping of our proposed method to the problem in the combinatorial optimization domain. The performance of the proposed method is then empirically assessed using 62 benchmark problems taken from the Operations Research Library (OR-Library). The solution quality is measured based on “Best”, “Average”, “Standard Deviation (S.D.)”, and “Relative Percent Error (RPE)” of the objective value. The results demonstrate that the proposed method is able to produce higher quality solutions than the current state-of-the-art heuristic-based algorithms.

Read more | August 8, 2014

 


The best-so-far ABC with multiple patrilines for clustering problems

Anan Banharnsakun, BooncharoenSirinaovakul, TiraneeAchalakul

 





Abstract

Clustering is an important process in many application domains such as machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics. The main objective of clustering is to search for hidden patterns that may exist in datasets. Since the clustering problem is considered to be NP-hard, previous research has applied bio-inspired heuristic methods to solve such problems. In this paper we propose an effective method for clustering using an algorithm inspired by the decision making processes of bee swarms. The algorithm is called the Best-so-far Artificial Bee Colony with multiple patrilines. In the Best-so-far method, the solution direction is biased toward the Best-so-far solution rather than a neighboring solution proposed in the original Artificial Bee Colony algorithm. We introduce another bee-inspired concept called multiple patrilines to further improve the diversity of solutions and allow the calculations to be distributed among multiple computing units. We empirically assess the performance of our proposed method on several standard datasets taken from the UCI Machine Learning Repository. The results show that the proposed method produces solutions that are as good as or better than the current state-of-the-art clustering techniques reported in the literature. Furthermore, to demonstrate the computing performance and scalability of the algorithm, we assess the algorithm on a large disk drive manufacturing dataset. The results indicate that our distributed Best-so-far approach is scalable and produces good solutions while significantly improving the processing time.

Read more | August 8, 2014

Object detection based on template matching through Use of best-so-far ABC

Anan Banharnsakun, Supannee Tanathong

Abstract

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution.

Read more | August 8, 2014

 

Reducing bioinformatics data dimension with ABC-kNN

Thananan Prasartvit, AnanBanharnsakun, Boonserm Kaewkamnerdpong, Tiranee Achalakul

 







Abstract

Analyzing a large amount of data often consumes extensive computational resources and execution time. However, sometime all data features do not equally contribute to the end results. Thus, it is plausible to identify the major contributing features and use them as representatives of the data. Other features with low contribution can be eliminated to reduce the time/resource consumption in data analysis. One of the promising application domains for such a feature selection process is bioinformatics. The need for dimension reduction, which is the process to reduce unnecessary features from the original data, arises because biological data can be massive, with tens of thousands of features to be explored. The objective of this study is to design an effective algorithm that can selectively remove irrelevant dimensions from data describing complex biological processes while preserving the semantics of the original data. This research proposes the adoption of the Artificial Bee Colony (ABC) as a novel method for data dimension reduction in classification problems. ABC, an efficient heuristic method based on swarm intelligence, is used to select the optimal subset of dimensions from the original high-dimensional data while retaining a subset that satisfies the defined objective. The k-Nearest Neighbor (kNN) method is then used for fitness evaluation within the ABC framework. In this research, ABC and kNN have been modified and bundled together to create an effective dimension reduction method. The proposed algorithm is validated in two distinct application domains: Gene expression analysis, and autistic behaviors study. The experimental results exhibit good solution quality as well as good computational performance.

Read more | August 8, 2014

Multi-focus image fusion using best-so far ABC strategies

Anan Banharnsakun

Abstract

Multi-focus image fusion is a process of combining a set of images that have been captured from the same scene but with different focuses in order to construct an additional sharper image. This process plays an important role in the image processing and machine vision fields. Various algorithms have been developed for this task. The key challenge in the design of multi-focus image fusion algorithms is how to evaluate the local content information of each image from the source images. Simple, but effective, block-based techniques at pixel level are widely used for multi-focus image fusion. However, a fixed block size may not be applicable to every application. A block size that is too small or too large is also not desirable. Hence, optimization of the block size is necessary in order to obtain a fused image that comprises the sharper parts of the source images. Recently, a number of techniques based on evolutionary computation have been applied to block-based multi-focus image fusion. The artificial bee colony (ABC) algorithm is one of the more popular evolutionary computational approaches used to find an optimal solution. In this paper, an efficient and robust block-based multi-focus image fusion method based on the optimal selection of sharper image blocks from source images using best-so-far ABC strategies is proposed. Experiment results show that the proposed method is able to provide good results and outperforms other conventional methods, both visually and quantitatively.



Read more | September 10, 2015

Hybrid ABC-ANN for pavement surface distress detection and classification

Anan Banharnsakun

Abstract

Pavement condition assessment plays an important role in the process of road maintenance and rehabilitation. However, the traditional road inspection procedure is mostly performed manually, which is labor-intensive and time-consuming. The development of automated detection and classification of distress on the pavement surface system is thus necessary. In this paper, a pavement surface distress detection and classification system using a hybrid between the artificial bee colony (ABC) algorithm and an artificial neural network (ANN), called “ABC-ANN”, is proposed. In the proposed method, first, after the pavement image is captured, it will be segmented into distressed and non-distressed regions based on a thresholding method. The optimal threshold value used for segmentation in this step will be obtained from the ABC algorithm. Next, the features, including the vertical distress measure, the horizontal distress measure, and the total number of distress pixels, are extracted from a distressed region and used to provide the input to the ANN. Finally, based on these input features, the ANN will be employed to classify an area of distress as a specific type of distress, which includes transversal crack, longitudinal crack, and pothole. The experimental results demonstrate that the proposed approach works well for pavement distress detection and can classify distress types in pavement images with reasonable accuracy. The accuracy obtained by the proposed ABC-ANN method achieves 20 % increase compared with existing algorithms.



Read more | December 10, 2015

Drug delivery based on swarm microrobots

Anan Banharnsakun, Tiranee Achalakul, Romesh C. Batra

Abstract

Advances in the development of technology have led to microrobots applications in medical fields. Drug delivery is one of these applications in which microrobots deliver a pharmaceutical compound to targeted cells. Chemotherapy and its side effects can then be minimized by this method. Two major constraints, however, must be considered: the robot’s onboard energy supply and the time needed for drug delivery. Furthermore, a microrobot must avoid biological restricted areas which we treat as obstacles in the path. The main objectives of this work were to find optimal paths to targeted cells and avoid collision with obstacles in the paths under a dynamic environment. In this study, we controlled motion of microrobots based on the concept of swarm intelligence. Artificial Bee Colony (ABC), the Best-so-far ABC, and the Particle Swarm Optimization (PSO) methods were employed to implement the collision detection and the boundary distance detection modules. Forces that drove or resisted blood flow as well as pressure in blood vessels were considered to approximate the effects of the environment on the microrobots. Numerical experiments were conducted using various obstacle environments. The results confirm that the proposed approaches were successful in avoiding obstacles and optimizing the energy consumption used to reach the target.

Read more | June 23, 2016

 







A MapReduce-based artificial bee colony for large-scale data clustering

AnanBanharnsakun

 



Abstract

The progress of technology has been a significant factor in increasing the growth of digital data. Therefore, good data analysis is a necessity for making better decisions. Clustering is one of the most important elements in the field of data analysis. However, the clustering of very large datasets is considered a primary concern. The improvement of computational models along with the ability to cluster huge volumes of data within a reasonable amount of time is thus required. MapReduce is a powerful programming model and an associated implement for processing large datasets with a parallel, distributed algorithm in a computing cluster. In this paper, a MapReduce-based artificial bee colony called MR-ABC is proposed for data clustering. The ABC is implemented based on the MapReduce model in the Hadoop framework and utilized to optimize the assignment of the large data instances to clusters with the objective of minimizing the sum of the squared Euclidean distance between each data instance and the centroid of the cluster to which it belongs. The experimental results demonstrate that our proposed algorithm is well-suited for dealing with massive amounts of data, while the quality level of the clustering results is still maintained.

Read more | August 11, 2016

A hierarchical clustering of features approach for vehicle tracking in traffic environments

Anan Banharnsakun, Supannee Tanathong

Abstract

Purpose - Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking. Especially in a traffic video monitoring system, vehicle detection is an essential and challenging task. In the previous studies, many vehicle detection methods have been presented. These proposed approaches mostly used either motion information or characteristic information to detect vehicles. Although these methods are effective in detecting vehicles, their detection accuracy still needs to be improved. Moreover, the headlights and windshields, which are used as the vehicle features for detection in these methods, are easily obscured in some traffic conditions.
Design/Methodology/Approach - First, each frame will be captured from a video sequence and then is performed the background subtraction by using the Mixture-of-Gaussians background model. Next, the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks. These feature blocks will be used to track the moving objects frame by frame.
Findings - Using the proposed method, it is able to detect the vehicles in both day-time and night-time scenarios with a 95% accuracy rate and can cope with irrelevant movement (waving trees), which has to be deemed as background. In addition, the proposed method is able to deal with different vehicle shapes such as cars, vans, and motorcycles.
Originality/value - This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.

Read more | September 23, 2016

 







Feature point matching based on ABC-NCC algorithm

Anan Banharnsakun

Abstract

Feature point matching is the process of finding an optimal spatial transformation that aligns two arbitrary sets of feature points. It is one of the most fundamental problems in the computer vision domain and is frequently used in object recognition, image registration, camera self-calibration, and so on. Critical to most feature point matching techniques is the determination of correspondence between spatially localized feature points within each image. Moreover, there can be many feature points in either set that have no counterparts in the other. A robust and effective method for feature point matching is thus required and is still a challenge. In this work, an artificial bee colony (ABC) with a normalized cross-correlation (NCC) algorithm called “ABC-NCC” for feature point matching is presented. In this proposed method, both the size and the orientation of the correlation window used for calculating the NCC are determined according to the scale and the rotation direction of the interest points, which are optimized by the ABC algorithm. Experimental results obtained by our method show that the proposed approach works well for feature point matching and outperforms existing algorithms.



Read more | April 5, 2017

Conferences




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