A Novel Parallel Approach of Cuckoo Search using MapReduce

DOI

10.2991/ccit-14.2014.31How to use a DOI?

Keywords

MapReduce, Cuckoo search, Metaheuristic, Big data, Cloud computing

Abstract

Optimization is a very common problem both in theoretical and practical scenes. However, at the same time, it is also been classified as a NP-hard problem in most cases, complex and hardware resource consuming. In order to evaluate the optimal value in a limited time and with a limited computing resource, people often choose metaheuristic methods to search it. Cuckoo search is a newly proposed metaheuristic method, which is suggested very compromising in recent works. In this paper, we invent a novel approach to accommodate cuckoo search to the analysis of big data by using latest cloud computing technical, which is called MapReduce paradigm. During the implementation of our method, we use HADOOP platform as the backend MapReduce engine. At the last part, through a series of simulation experiments, we prove that our approach has a much better runtime performance when processing large dataset.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xingjian Xu
AU  - Zhaohua Ji
AU  - Fangfang Yuan
AU  - Xiaoqin Liu
PY  - 2014/01
DA  - 2014/01
TI  - A Novel Parallel Approach of Cuckoo Search using MapReduce
BT  - Proceedings of the 2014 International Conference on Computer, Communications and Information Technology
PB  - Atlantis Press
SP  - 114
EP  - 117
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccit-14.2014.31
DO  - 10.2991/ccit-14.2014.31
ID  - Xu2014/01
ER  -