A hybrid optimization approach using Evolutionary Computing and Map Reduce Architecture

被引：1

作者：

Lohani, Bhanu Prakash ^{[1
]}

Singh, Ajit ^{[2
]}

Bibhu, Vimal ^{[3
]}

机构：

[1] UTU, Dept CSE, Dehra Dun, Uttarakhand, India

[2] BTKIT, Dept CSE, Dwarahat, Uttarakhand, India

[3] Amity Univ Gr Noida, Dept CSE, Greater Noida, Uttar Pradesh, India

来源：

PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATION ENGINEERING (ICACCE-2019) | 2019年

关键词：

Big Data; Evolutionary Computing; Map-Reduce; Optimization; Parallel Processing; Decision making; Data Analysis;

D O I：

10.1109/icacce46606.2019.9080013

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Big data and its application are very popular now a day because due to technological advancement in the field of Information technology the amount of data generation rate is too high. Data Mining & analysis is done for making better decision with respect to the data generated from different sources. Decision making for the route in the traffic environment is also a problem of Big Data because it creates a huge amount of data so we need to optimize or seprate the data with respect to various criteria, for this we need to know about the optimization algorithms. Evolutionary Computing is a branch of Computer science which works upon the concept of Darwinian evolution and the evolutionary computing algorithms are used to find the optimal solution. The review of optimization algorithm is presented in this paper. For the optimization process we have selected Ant Colony optimization algorithm to find the best route and implemented the algorithm using the concept of Map reduce architecture for parallel processing.

引用

页数：5

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