Emrah Hancer

Dynamic clustering with improved binary artificial bee colony algorithm

We proposed an improved binary artificial bee colony algorithm (IDisABC).We examined the proposed algorithm on dynamic clustering.Data and image clustering benchmark problems are chosen for experiments.The obtained results are compared with K-means, FCM, GA, DisABC, DCPSO. One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is accepted as one of the simple, novel and efficient binary variant of the artificial bee colony, the applied mechanism for generating new solutions concerning to the information of similarity between the solutions only consider one similarity case i.e. it does not handle all similarity cases. To cover this issue, new solution generation mechanism of the discrete artificial bee colony is enhanced using all similarity cases through the genetically inspired components. Furthermore, the superiority of the proposed algorithm is demonstrated by comparing it with the basic discrete artificial bee colony, binary particle swarm optimization, genetic algorithm in dynamic (automatic) clustering, in which the number of clusters is determined automatically i.e. it does not need to be specified in contrast to the classical techniques. Not only evolutionary computation based algorithms, but also classical approaches such as fuzzy C-means and K-means are employed to put forward the effectiveness of the proposed approach in clustering. The obtained results indicate that the discrete artificial bee colony with the enhanced solution generator component is able to reach more valuable solutions than the other algorithms in dynamic clustering, which is strongly accepted as one of the most difficult NP-hard problem by researchers.

A novel binary artificial bee colony algorithm based on genetic operators

This study proposes a novel binary version of the artificial bee colony algorithm based on genetic operators (GB-ABC) such as crossover and swap to solve binary optimization problems. Integrated to the neighbourhood searching mechanism of the basic ABC algorithm, the modification comprises four stages: (1) In neighbourhood of a (current) food source, randomly select two food sources from population and generate a solution including zeros (Zero) outside the population; (2) apply two-point crossover operator between the current, two neighbourhood, global best and Zero food sources to create children food sources; (3) apply swap operator to the children food sources to generate grandchildren food sources; and (4) select the best food source as a neighbourhood food source of the current solution among the children and grandchildren food sources. In this way, the global-local search ability of the basic ABC algorithm is improved in binary domain. The effectiveness of the proposed algorithm GB-ABC is tested on two well-known binary optimization problems: dynamic image clustering and 0-1 knapsack problems. The obtained results clearly indicate that GB-ABC is the most suitable algorithm in binary optimization when compared with the other well-known existing binary optimization algorithms. In addition, the achievement of the proposed algorithm is supported by applying it to the CEC2005 benchmark numerical problems.

Artificial Bee Colony based image clustering method

Clustering plays important role in many areas such as medical applications, pattern recognition, image analysis and statistical data analysis. Image clustering is an application of image analysis in order to support high-level description of image content for image understanding where the goal is finding a mapping of the images into clusters. This paper presents an Artificial Bee Colony (ABC) based image clustering method to find clusters of an image where the number of clusters is specified. The proposed method is applied to three benchmark images and the performance of it is analysed by comparing the results of K-means and Particle Swarm Optimization (PSO) algorithms. The comprehensive results demonstrate both analytically and visually that ABC algorithm can be successfully applied to image clustering.

Improved clustering criterion for image clustering with artificial bee colony algorithm

In this paper, a new objective function is proposed for image clustering and is applied with the artificial bee colony (ABC) algorithm, the particle swarm optimization algorithm and the genetic algorithm. The performance of the proposed objective function is tested on seven benchmark images by comparing it with the three well-known objective functions in the literature and the K-means algorithm in terms of separateness and compactness which are the main criterions of the clustering problem. Moreover, the Davies–Bouldin Index and the XB Index are also employed to compare the quality of the proposed objective function with the other objective functions. The simulated results show that the ABC-based image clustering method with the improved objective function obtains well-distinguished clusters.

Extraction of brain tumors from MRI images with artificial bee colony based segmentation methodology

Image segmentation plays significant role in medical applications to extract or detect suspicious regions. In this paper, a new image segmentation methodology based on artificial bee colony algorithm (ABC) is proposed to extract brain tumors from magnetic reasoning imaging (MRI), one of the most useful tools used for diagnosing and treating medical cases. The proposed methodology comprises three phases: enhancement of the original MRI image (pre-processing), segmentation with the ABC based image clustering method (processing), and extraction of brain tumors (post-processing). The proposed methodology is compared and analyzed on totally 9 MRI images shooting in different positions from a patient with the methodologies based on K-means, Fuzzy C-means and genetic algorithms. It is observed from the experimental studies that the segmentation process with the ABC algorithm obtains both visually and numerically best results.

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

Abstract Todays data mostly does not include the knowledge of cluster number. Therefore, it is not possible to use conventional clustering approaches to partition todays data, i.e., it is necessary to use the approaches that automatically determine the cluster number or cluster structure. Although there has been a considerable attempt to analyze and categorize clustering algorithms, it is difficult to find a survey paper in the literature that has thoroughly focused on the determination of cluster number. This significant issue motivates us to introduce concepts and review methods related to automatic cluster evolution from a theoretical perspective in this study.

A new approach to the reconstruction of contour lines extracted from topographic maps

It is known that after segmentation and morphological operations on topographic maps, gaps occur in contour lines. It is also well known that filling these gaps and reconstruction of contour lines with high accuracy is not an easy problem. In this paper, a nontrivial semi-automatic approach to solve this problem is proposed. The main idea of the proposed approach is based on local and geometric properties such as (1) parabolic and opposite directions, (2) the differences of y-ordinate of end points, (3) changing the directions of x-axis and y-ordinate to the nearest clockwise direction and (4) avoiding the use of the second end point of a small piece of any contour line in the same mask if its other end point is used. The proposed approach was implemented on the base of many topographic maps with different resolutions and complexity. The obtained results show that the proposed approach increases accuracy and performance.

A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information

Feature selection often involves two conflicting objectives of minimizing the feature subset size and the maximizing the classification accuracy. In this paper, a multi-objective artificial bee colony (MOABC) framework is developed for feature selection in classification, and a new fuzzy mutual information based criterion is proposed to evaluate the relevance of feature subsets. Three new multi-objective feature selection approaches are proposed by integrating MOABC with three filter fitness evaluation criteria, which are mutual information, fuzzy mutual information and the proposed fuzzy mutual information. The proposed multi-objective feature selection approaches are examined by comparing them with three single-objective ABC-based feature selection approaches on six commonly used datasets. The results show that the proposed approaches are able to achieve better performance than the original feature set in terms of the classification accuracy and the number of features. By using the same evaluation criterion, the proposed multi-objective algorithms generally perform better than the single-objective methods, especially in terms of reducing the number of features. Furthermore, the proposed fuzzy mutual information criterion outperforms mutual information and the original fuzzy mutual information in both single-objective and multi-objective manners. This work is the first study on multi-objective ABC for filter feature selection in classification, which shows that multi-objective ABC can be effectively used to address feature selection problems.

Pareto front feature selection based on artificial bee colony optimization

Abstract Feature selection has two major conflicting aims, i.e., to maximize the classification performance and to minimize the number of selected features to overcome the curse of dimensionality. To balance their trade-off, feature selection can be handled as a multi-objective problem. In this paper, a feature selection approach is proposed based on a new multi-objective artificial bee colony algorithm integrated with non-dominated sorting procedure and genetic operators. Two different implementations of the proposed approach are developed: ABC with binary representation and ABC with continuous representation. Their performance are examined on 12 benchmark datasets and the results are compared with those of linear forward selection, greedy stepwise backward selection, two single objective ABC algorithms and three well-known multi-objective evolutionary computation algorithms. The results show that the proposed approach with the binary representation outperformed the other methods in terms of both the dimensionality reduction and the classification accuracy.

Advanced contour reconnection in scanned topographic maps

This paper presents an advanced reconnection of contour lines extracted from scanned topographic maps. In other words, this paper refers to the problem of gap filling. New properties such as 1) controlling a search mask for even number end points, 2) using the parabolic directions, and 3) back tracing less than ten pixels are added to the classical semi-automatic method to improve performance and accuracy. Proposed method has been applied to many different maps and the results of two implementations are given in this paper. An analysis and comparison of obtained results show that proposed method improves run time and accuracy about 22–44% and 12–14%, respectively.