Integration of data sources to build a Data warehouse (DW), refers to the task of developing a common schema as well as data transformation solutions for a number of data sources with related content. The large number and size of modern data sources make the integration process cumbersome. In such cases dimensionality of the data is reduced prior to populating the DWs. Attribute subset selection on the basis of relevance analysis is one way to reduce the dimensionality. Relevance analysis of attribute is done by means of correlation analysis, which detects the attributes (redundant) that do not have significant contribution in the characteristics of whole data of concern. After which the redundant attribute or attribute strongly correlated to some other attribute is disqualified to be the part of DW. Automated tools based on the existing methods for attribute subset selection may not yield optimal set of attributes, which may degrade the performance of DW. Various researchers have used GA, as an optimization tool but most of them use GA to search the optimal technique amongst the available techniques for attribute selection. This paper formulates and validates a method for selecting optimal attribute subset based on correlation using Genetic algorithm (GA), where GA is used as optimal search tool for selecting subset of attributes..
General Terms:Data Warehousing, Data Mining, Genetic Algorithms.
Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these.
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