Pramod Kumar Singh

An Efficient Prevention of Black Hole Problem in AODV Routing Protocol in MANET

Interest in the area of Mobile Ad-hoc Network (MANET) is growing since last few years because of its practical applications and requirement of communication in mobile devices. However, in comparison to wired network or infrastructure-based wireless network, MANET is particularly vulnerable to security attacks due to its fundamental characteristics, e.g., the open medium, dynamic network topology, autonomous terminal, lack of centralized monitoring and management. The black hole attack is one of such security risks. In this attack, a malicious node falsely advertise shortest path to the destination node with an intension to disrupt the communication. In this paper, we propose a solution to the black hole attack in one of the most prominent routing algorithm, ad-hoc on demand distance vector (AODV) routing, for the MANETs. The proposed method uses promiscuous mode to detect malicious node (black hole) and propagates the information of malicious node to all the other nodes in the network. The simulation results show the efficacy of the proposed method as throughput of the network does not deteriorate in presence of the back holes.

Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering

Graphical abstractDisplay Omitted HighlightsA feature selection method based on binary particle swarm optimization is presented.Fitness based adaptive inertia weight is integrated with the binary particle swarm optimization to dynamically control the exploration and exploitation of the particle in the search space.Opposition and mutation are integrated with the binary particle swarm optimization improve its search capability.The performance of the clustering algorithm improves with the features selected by proposed method. Due to the ever increasing number of documents in the digital form, automated text clustering has become a promising method for the text analysis in last few decades. A major issue in the text clustering is high dimensionality of the feature space. Most of these features are irrelevant, redundant, and noisy that mislead the underlying algorithm. Therefore, feature selection is an essential step in the text clustering to reduce dimensionality of the feature space and to improve accuracy of the underlying clustering algorithm. In this paper, a hybrid intelligent algorithm, which combines the binary particle swarm optimization (BPSO) with opposition-based learning, chaotic map, fitness based dynamic inertia weight, and mutation, is proposed to solve feature selection problem in the text clustering. Here, fitness based dynamic inertia weight is integrated with the BPSO to control movement of the particles based on their current status, and the mutation and the chaotic strategy are applied to enhance the global search capability of the algorithm. Moreover, an opposition-based initialization is used to start with a set of promising and well-diversified solutions to achieve a better final solution. In addition, the opposition-based learning method is also used to generate opposite position of the gbest particle to get rid of the stagnation in the swarm. To prove effectiveness of the proposed method, experimental analysis is conducted on three different benchmark text datasets Reuters-21578, Classic4, and WebKB. The experimental results demonstrate that the proposed method selects more informative features set compared to the competitive methods as it attains higher clustering accuracy. Moreover, it also improves convergence speed of the BPSO.

Chaotic gradient artificial bee colony for text clustering

Text clustering is widely used to create clusters of the digital documents. Selection of cluster centers plays an important role in the clustering. In this paper, we use artificial bee colony algorithm (ABC) to select appropriate cluster centers for creating clusters of the text documents. The ABC is a population-based nature-inspired algorithm, which simulates intelligent foraging behavior of the real honey bees and has been shown effective in solving many search and optimization problems. However, a major drawback of the algorithm is that it provides a good exploration of the search space at the cost of exploitation. In this paper, we improve search equation of the ABC and embed two local search paradigms namely chaotic local search and gradient search in the basic ABC to improve its exploitation capability. The proposed algorithm is named as chaotic gradient artificial bee colony. The effectiveness of the proposed algorithm is tested on three different benchmark text datasets namely Reuters-21,578, Classic4, and WebKB. The obtained results are compared with the ABC, a recent variant of the ABC namely gbest-guided ABC, a variant of the proposed methodology namely chaotic artificial bee colony, memetic ABC, and conventional clustering algorithm K-means. The empirical evaluation reveals very encouraging results in terms of the quality of solution and convergence speed.

A three-stage unsupervised dimension reduction method for text clustering

Feature selection is widely used in text clustering to reduce dimensions in the feature space. In this paper, we study and propose two-stage unsupervised feature selection methods to determine a subset of relevant features to improve accuracy of the underlying algorithm. We experiment with hybrid approach of feature selection—feature selection (FS–FS) and feature selection—feature extraction (FS–FE) methods. Initially, each feature in the document is scored on the basis of its importance for the clustering using two different feature selection methods individually Mean-Median (MM) and Mean Absolute Difference (MAD).In the second stage, in two different experiments, we hybridize them with a feature selection method absolute cosine (AC) and a feature extraction method principal component analysis (PCA) to further reduce the dimensions in the feature space. We perform comprehensive experiments to compare FS, FS–FS and FS–FE using k-mean clustering on Reuters-21578 dataset. The experimental results show that the two-stage feature selection methods are more effective to obtain good quality results by the underlying clustering algorithm. Additionally, we observe that FS–FE approach is superior to FS–FS approach.

NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering

Abstract This paper presents a non-dominated sorting based multi-objective artificial bee colony algorithm NSABC to solve multi-objective optimization problems. It is an extension of the artificial bee colony algorithm ABC, which is a single objective optimization algorithm, to the multi-objective optimization domain. It uses a novel approach in the employee bee phase to steer the solutions to simultaneously achieve both the orthogonal goals in the multi-objective optimization – convergence and diversity. The onlooker bee phase is similar to the ABC except for the fitness computation to exploit the promising solutions whereas there is no change in the scout bee phase, which is used to get rid of bad solutions and add diversity in the swarm by introducing random solutions. Along with a novel way of exploring new solutions, it uses non-dominated sorting and crowding distance, inspired by the NSGA-II, to maintain the best and diverse solutions in the swarm. It is tested on the 10 two-objective and three-objective unconstrained benchmark problems of varying nature and complexities from the CEC09 suite of test problems and is found better than or commensurable to thirteen state-of-the-art significant multi-objective optimization algorithms as well as other multi-objective variants of the ABC. Further, it is tested on the nine real-life data clustering problems considered from the UCI machine learning repository and proved itself better in comparison to the NSGA-II, MOVGA, and a recent multi-objective variant of the ABC named MOABC. Thus, it is observed that the NSABC is comparatively a simple, light, and powerful algorithm to solve multi-objective problems.

An effective multiobjective approach for hard partitional clustering

Clustering is an unsupervised classification method in the field of data mining. Many population based evolutionary and swarm intelligence optimization methods are proposed to optimize clustering solutions globally based on a single selected objective function which lead to produce a single best solution. In this sense, optimized solution is biased towards a single objective, hence it is not equally well to the data set having clusters of different geometrical properties. Thus, clustering having multiple objectives should be naturally optimized through multiobjective optimization methods for capturing different properties of the data set. To achieve this clustering goal, many multiobjective population based optimization methods, e.g., multiobjective genetic algorithm, mutiobjective particle swarm optimization (MOPSO), are proposed to obtain diverse tradeoff solutions in the pareto-front. As single directional diversity mechanism in particle swarm optimization converges prematurely to local optima, this paper presents a two-stage diversity mechanism in MOPSO to improve its exploratory capabilities by incorporating crossover operator of the genetic algorithm. External archive is used to store non-dominated solutions, which is further utilized to find one best solution having highest F-measure value at the end of the run. Two conceptually orthogonal internal measures SSE and connectedness are used to estimate the clustering quality. Results demonstrate effectiveness of the proposed method over its competitors MOPSO, non-dominated sorting genetic algorithm, and multiobjective artificial bee colony on seven real data sets from UCI machine learning repository.

Comparative Study of Artificial Bee Colony Algorithm and Real Coded Genetic Algorithm for Analysing Their Performances and Development of a New Algorithmic Framework

This paper compares performance of the artificial bee colony algorithm (ABC) and the real coded genetic algorithm (RCGA) on a suite of 9 standard benchmark problems. The problem suite comprises a diverse set of unimodal, multimodal and rotated multimodal numerical optimization functions and the comparison criteria include (i) solution quality, (ii) convergence speed, (iii) robustness, and (iv) scalability to test efficacy of the algorithms. To the best knowledge of the authors, such a comprehensive comparative study of the two algorithms is not available in the literature. An empirical study shows that the RCGA has advantages over the ABC in terms of all the criteria for the unimodal and the rotated multimodal functions. On other hand, the ABC outperforms the RCGA in terms of solution quality for the multimodal functions. Therefore, based on the insights gained out of this comparative study, the authors propose an algorithm ABC-GA with new algorithmic framework that comprises advantages of both the ABC and the GA. An empirical study of the proposed algorithm ABC-GA shows its promising performance as the obtained results are superior to both the comparative algorithms for all the problems in all the criteria.

Association rule mining using multi-objective evolutionary algorithms: Strengths and challenges

Association rule mining based on support and confidence generates a large number of rules. However, post analysis is required to obtain interesting rules as many of the generated rules are useless. We pose mining association rules as multi-objective optimization problem where objective functions are rule interestingness measures and use NSGA-II, a well known multi-objective evolutionary algorithm (MOEA), to solve the problem. We compare our results vis-à-vis results obtained by a traditional rule mining algorithm - Apriori and contrary to the other works reported in the literature clearly highlight the quality of obtained rules and challenges while using MOEAs for mining association rules. Though none of the algorithm emerged as clear winner, some of the rules obtained by MOEA could not be obtained by traditional data mining algorithm. We treat the whole process from data mining perspective and discuss the pitfalls responsible for relatively poor performance of the MOEA which has been shown as a good performer in other paradigms.

Empirical Study of Grey Wolf Optimizer

In this paper, the authors empirically investigate performance of the grey wolf optimizer (GWO). A test suite of six non-linear benchmark functions, well studied in the swarm and the evolutionary optimization literature, is selected to highlight the findings. The test suite contains three unimodal and three multimodal functions. The experimental results demonstrate the advantages and weaknesses of the GWO. In case of unimodal problems, initially it hastens towards the optimal solution but soon slows down because of the diversity problem. A similar behaviour is seen in case of multimodal problems with a difference that because of its behaviour it easily sticks to local optima, loses its diversity and stops any further progress. The reason is that it lacks information sharing in the pack. This insight led the authors to propose a modified GWO called the modified grey wolf optimizer (MGWO). An empirical study of the proposed algorithm MGWO shows its promising performance as the obtained results are superior to the GWO for all the test functions.

Partitional Algorithms for Hard Clustering Using Evolutionary and Swarm Intelligence Methods: A Survey

Evolutionary and swarm intelligence methods attracted attention and gained popularity among the data mining researchers due to their expedient implementation, parallel nature, ability to search global optima and other advantages over conventional techniques. These methods along with their variants and hybrid approaches have emerged as worthwhile class of methods for clustering. Clustering is an unsupervised classification method. The partitional clustering algorithms look for hard clustering; they decompose the dataset into a set of disjoint clusters. This paper describes a brief review of evolutionary and swarm intelligence methods with their variants and hybrid approaches designed for partitional clustering algorithms for hard clustering of datasets.