This paper presents a new approach to determine the optimal number and arrangement of power quality monitors (PQMs) for voltage sag detection. It is necessary to determine the optimal number and arrangement of PQMs si...
详细信息
ISBN:
(纸本)9781728138169
This paper presents a new approach to determine the optimal number and arrangement of power quality monitors (PQMs) for voltage sag detection. It is necessary to determine the optimal number and arrangement of PQMs since their installation at all buses in a network is an uneconomical option due to relatively high price of PQMs. The appropriate mathematical model, that describes the considered optimization problem, is created by using the concept of topological monitor reach area. A new definition of the cost function is presented in the paper in order to simultaneously determine the required number of PQMs and their best arrangement. Also, the effect of setting different values of monitor's coverage control parameter on the obtained results is analyzed. Four optimization methods are implemented to solve the considered problem: binary bat algorithm, binary Dragonfly algorithm, binary Particle Swarm Optimization and Genetic algorithm. The presented approach is tested on the IEEE 34-node test system. Simulations proved that the binary bat algorithm has the best performance in terms of computational time, convergence and the probability rate of finding the global optimum.
In this paper, a metaheuristic algorithm has been introduced for software usability feature selection and evaluation. Usability is becoming one of the most significant aspects of quality of software. The term 'usa...
详细信息
In this paper, a metaheuristic algorithm has been introduced for software usability feature selection and evaluation. Usability is becoming one of the most significant aspects of quality of software. The term 'usability' has already been defined by the authors in their previous work in a reference to the hierarchical software usability model. This model combines various usability factors and features in a hierarchical manner. Here, we introduced MBbat (Modified binary bat algorithm) for usability feature selection to get an optimal solution for the search of useful usability features out of a given set of usability features. MBbat is an extension of binary bat algorithm(BBA) which is based on the bat's behavior and to the best of our knowledge, this algorithm is introduced for the first time in software engineering practices. The selected number of features and accuracy of proposed MBbatalgorithm is compared with the original BBA and the proposed metaheuristic algorithm outperforms the original BBA as it generates a fewer number of selected features and having low accuracy. (C) 2017 Elsevier B.V. All rights reserved.
Proper location management of the mobile users in the mobile cellular networks is of great importance in this era of wireless communications. This paper has solved the location management issue based on the reporting ...
详细信息
ISBN:
(纸本)9781538629895
Proper location management of the mobile users in the mobile cellular networks is of great importance in this era of wireless communications. This paper has solved the location management issue based on the reporting cell planning approach. The mobile location management comprises of two perspectives: location update and paging and it is developed as a cost optimization problem. In this paper, we propose a new metaheuristic popular, nature-inspired batalgorithm with the objective of minimizing the involved location management cost. We have compared the proposed approach with the state-of-the-art approaches available M the literature. Here the applicability of the batalgorithm to the location management problem has been addressed using the reference networks as well the realistic networks of Odisha state. The computational results clearly show that the proposed approach performs as good as other approaches in terms of solution quality and has an added advantage of faster convergence.
1Classification is a central problem in the fields of data mining and machine learning. Using a training set of labeled instances, the task is to build a model (classifier) that can be used to predict the class of new...
详细信息
ISBN:
(纸本)9781509056866
1Classification is a central problem in the fields of data mining and machine learning. Using a training set of labeled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more effective classifiers. Searching for the frequent pattern within a specific sequence has become a much needed task in various sectors. Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. In the existing work, unsupervised feature selection methods using Artificial Bee Colony Optimization algorithm, batalgorithm and Ant Colony Optimization have been introduced. We have compared these three algorithms and concluded that batalgorithm proves to be better in performance than the rest. The proposed system will use a novel method to select subset of features from unlabelled data using batalgorithm with one of the clustering algorithm and develop an expert information retrieval system.
In this work, an attempt has been made to investigate the effectiveness of binary bat algorithm as a feature selection method to classify sEMG signals under fatigue and nonfatigue conditions. The sEMG signals are reco...
详细信息
ISBN:
(纸本)9783319202945;9783319202938
In this work, an attempt has been made to investigate the effectiveness of binary bat algorithm as a feature selection method to classify sEMG signals under fatigue and nonfatigue conditions. The sEMG signals are recorded from the biceps brachii muscle of 50 healthy volunteers. The signals are preprocessed and then multiscale Renyi entropy based feature are extracted. The binary bat algorithm is used for feature selection and the effectiveness is compared with information gain based ranker. The performance of the feature selection algorithms are validated by performing classification using Naive Bayes, and least square support vector machines. The results show a decreasing trend in the multiscale Renyi entropy with increase in scale. Additionally, higher entropy values where observed in fatigue condition. The classification results showed that a maximum accuracy of 86.66 % is obtained with least square SVM and binary bat algorithm. It appears that, this technique is useful in identifying muscle fatigue in varied clinical conditions.
Information Security Assurance implies ensuring the integrity, confidentiality and availability of critical assets for an organization. The large amount of events to monitor in a fluid system in terms of topology and ...
详细信息
ISBN:
(纸本)9781467367974
Information Security Assurance implies ensuring the integrity, confidentiality and availability of critical assets for an organization. The large amount of events to monitor in a fluid system in terms of topology and variety of new hardware or software, overwhelms monitoring controls. Furthermore, the multi-facets of cyber threats today makes it difficult even for security experts to handle and keep up-to-date. Hence, automatic "intelligent" tools are needed to address these issues. In this paper, we describe a 'work in progress' contribution on intelligent based approach to mitigating security threats. The main contribution of this work is an anomaly based IDS model with active response that combines artificial immune systems and swarm intelligence with the SVM classifier. Test results for the NSL-KDD dataset prove the proposed approach can outperform the standard classifier in terms of attack detection rate and false alarm rate, while reducing the number of features in the dataset.
As new security intrusions arise so does the demand for viable intrusion detection systems. These solutions must deal with huge data volumes, high speed network traffics and countervail new and various types of securi...
详细信息
ISBN:
(纸本)9789898565952
As new security intrusions arise so does the demand for viable intrusion detection systems. These solutions must deal with huge data volumes, high speed network traffics and countervail new and various types of security threats. In this paper we combine existing technologies to construct an Anomaly based Intrusion Detection System. Our approach improves the Support Vector Machine classifier by exploiting the advantages of a new swarm intelligence algorithm inspired by the environment of microbats (batalgorithm). The main contribution of our paper is the novel feature selection model based on binary bat algorithm with Levy flights. To test our model we use the NSL-KDD data set and empirically prove that Levy flights can upgrade the exploration of standard binary bat algorithm. Furthermore, our approach succeeds to enhance the default SVM classifier and we obtain good performance measures in terms of accuracy (90.06%), attack detection rate (95.05%) and false alarm rate (4.4%) for unknown attacks.
暂无评论