Anan Banharnsakun

The best-so-far selection in Artificial Bee Colony algorithm

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.

Job Shop Scheduling with the Best-so-far ABC

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.

ABC-GSX: A hybrid method for solving the Traveling Salesman Problem

An optimization problem is a problem of finding the best solution from all possible solutions. In most computer science and mathematical applications, the decision to select the best solution is not polynomially bounded. Heuristics approaches are thus often considered to solve such NP-hard problems. In our work, we focus on developing a heuristic method to solve a combinatorial optimization problem known as the Traveling Salesman Problem or TSP. Our technique implements the Artificial Bee Colony algorithm, which is inspired by the decision making process of the honey bees in finding optimal food sources. We extend the ABC algorithm with Greedy Subtour Crossover to improve the precision. In this hybrid procedure, the exploitation process in the ABC algorithm is improved upon by the Greedy Subtour Crossover method. The new proposed method is called ABC-GSX. We then empirically assess performance of our proposed work using functions from a standard TSP library. Experimental results show improvements in both precision and computational time compared to techniques presented in recent literatures.

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

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.

Artificial bee colony algorithm on distributed environments

Artificial Bee Colony (ABC) is a metaheuristic approach in which a colony of artificial bees cooperates in finding good solutions for numerical optimization problems. ABC is adopted widely for use in several domains of solution optimization. However, the algorithm generally requires a considerably large computational time and resources. In order to enhance the performance of this algorithm for a large problem size, we introduce a distributed version of ABC. In our parallel algorithm, the entire bee colony is decomposed into several subgroups. Each subgroup then performs a local search concurrently on each processor node. The local best solutions are then exchanged among processor nodes. The algorithm implementation utilizes the message passing technique as a communication paradigm. We then empirically assess the performance based on both result accuracy and algorithms efficiency. The experimental results show improvement in both solution quality and computing time when comparing to the sequential ABC algorithm.

Reducing bioinformatics data dimension with ABC-kNN

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.

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

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.

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

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.

Hybrid ABC-ANN for pavement surface distress detection and classification

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 20xa0% increase compared with existing algorithms.

Target finding and obstacle avoidance algorithm for microrobot swarms

Advances in the development of nanotechnology 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. Two major constraints, however, must be considered: the robots onboard energy supply and the time needed for drug delivery, which are related to the travel distance of microrobots. Furthermore, a microrobot must avoid biological restricted areas which we treat as obstacles in the path. The main objectives of this work are to find optimal paths to targeted cells and avoid collision with obstacles in the paths. In this study, we control motion of microrobots based on the concept of swarm intelligence. Artificial Bee Colony, the swarm-based optimization method, is employed to implement the collision detection and the boundary distance detection modules. The offline path distance optimization approach is also employed to improve the path planning results. Numerical experiments have been conducted using various obstacle environments that confirm that the proposed approach is successful in avoiding obstacles and optimizing the distance traveled to reach the target.