Advantages And Disadvantages Of Bat Algorithm

Benchmark functions used are minimization functions and are subdivided into the two groups i.e., unimodal and multimodal. Multimodal functions are also categorized into fixed dimension and high dimension multimodal functions. GSA is a heuristic optimization algorithm which has been gaining interest among the scientific community recently. GSA is a nature inspired algorithm which is based on the Newton’s law of gravity and the law of motion. GSA is grouped under the population based approach and is reported to be more intuitive. The algorithm is intended to improve the performance in the exploration and exploitation capabilities of a population based algorithm, based on gravity rules. However, recently GSA has been criticized for not

In the next paragraphs, this thesis paper discussed various factors of 3 mentioned MDSS like main motivation for the implementation of new MDSS, different data mining (DM) algorithms used, techniques used to improve the

Abstract-In this paper an efficient hybrid optimization approach is used to solve the optimal power flow problem. In the proposed approach particle swarm optimization along with ant colony optimization is used for setting of control variables for optimal power flow problem. The proposed approach is tested and examined in standard IEEE 30 bus test system. The various objective functions involved are fuel cost minimization, voltage profile improvement and transmission loss reduction. This proposed approach overcomes the disadvantages of ant colony optimization such as premature convergence and stagnating output.

As probably the most natural type of storing information is text and text mining is believed to have a commercial potential greater than that of data mining. The recent study indicated that 80% of a company’s information is including in text documents. Text mining, however, is also a more complex task as compare to data mining as it involves dealing with text data that are naturally unstructured and fuzzy. Text mining is a multidisciplinary area, involving information retrieval, text examination, information extraction, clustering, categorization, visualization, database technology, device learning, and data mining [2]. In Text Mining, patterns are extracted from natural language Textual Database. There are many methods of text mining. In general, the major approaches, based on the kinds of data they take as input,

Swarm robotics is the new approach to the coordination of multi-robot systems that consists of many relatively smaller robots. The inspiration of this is the social behaviour of many animals and insects like ants, geese etc. the terms “Swarm Intelligence” refers to the collective behaviour that is the outcome of the work of the smaller individuals, each acting autonomously. Swarm intelligence is a property of systems of non-intelligent exhibiting a collectively intelligent behaviour.

Data mining, association rule, fuzzy logic, neural network, particle swarm optimization, artificial bee colony algorithm and harmony search algorithm

In this study, we focused on the typical Computational Intelligence algorithm: Particle Swarm Optimization (PSO). PSO is a useful utility swarm intelligence

I will conduct a comparative analysis of multiple force-directed algorithms used to identify clusters in biological networks. The analysis will consider topics such as the algorithm process, amount of preprocessing, complexity, and flexibility of the algorithms for different types and sizes of data. K-Means, SPICi, Markov Clustering, RNSC, and PBD will be used for the comparison. I will identify the best algorithm according to my analysis for each type of input data studied.

Chuang and Chien [2004] proposed to cluster and organize users’ queries into a hierarchical structure of topic classes. A Hierarchical Agglomerative Clustering (HAC) [25] algorithm is first employed to construct a binary-tree cluster hierarchy. The binary-tree hierarchy is then partitioned in order to create subhierarchies forming a multiway-tree cluster hierarchy like the hierarchical organization of Yahoo [6] and DMOZ [3].

A Hybrid Cuckoo Algorithm and Practice Swarm Optimization for Planning of Parking Lots in Distribution systems

Abstract— Data mining is the method of extracting the data from large database. Various data mining techniques are clustering, classification, association analysis, regression, summarization, time series analysis and sequence analysis, etc. Clustering is one of the important tasks in mining and is said to be unsupervised classification. Clustering is the techniques which is used to group similar objects or processes. In this work four clustering algorithms (K-Means, Farthest first, EM, Hierarchal) have been analyzed to cluster the data and to find the outliers based on the number of clusters. Here the WEKA (Waikato Environment for Knowledge Analysis) for analyzing the clustering techniques. Here the time, Clustered and un-clustered

Abstract-This paper gives a brief description of the above titled paper. Data clustering is one of the most widely used method for various applications. And parallelizing these time-consuming applications is of quite importance. This paper brings out an additional feature of handling input data of various dimensions and thus accordingly handle it.

Abstract— The swarm intelligence plays vital role in feature reduction process in cyber-attack detection. The family of swarm intelligence gives bucket of algorithm for the processing of feature reduction such as ant colony optimization, particle swarm optimization and many more. In family of swarm new algorithm is called glowworm optimization algorithm based on the concept of luciferin. The luciferin collects the similar agent of glow and proceeds the minimum distance for the processing of lights. Such concept used for the reduction of feature in cyber-attack classification. The reduce attribute classified by well know classifier is called support vector machine. The combination of support vector machine and glowworm swarm optimization performs very well in compression of pervious feature reduction technique. The proposed algorithm is implemented in MATLAB software, for the validation of algorithm used KDDCUP99 dataset.

Document clustering is way of automatic organization of documents into clusters so that documents within a cluster have high similarity in comparison to documents in other clusters. It is measuring similarity between documents and grouping similar documents together. The study of similarity measure for clustering is initially motivated by a research on automated text categorization. There was several similarity measures used for document similarity. It provides client representation and visualization of the documents; thus helps in easy navigation also. It has been used intensively because of its wide applicability in various areas such as web mining, search engines, and information retrieval. The key of organizing data in such a way is to improve data availability and to fasten data access, so that web information retrieval and content delivery on the web are improved. The main idea is to improve the accessibility and usability of text mining for various applications. By optimizing similarity measures the optimal clusters can be formed thus performance is improved.

3Assistant Professor, Dept. Of Computer Science & Engineering, CT Institute of Technology & Research, Jalandhar, Punjab 144008, India