Review of Evolutionary Algorithms based on parallel computing paradigm

International Journal of Computer Science and Engineering

© 2017 by SSRG - IJCSE Journal

Volume 4 Issue 6

Year of Publication : 2017

Authors : Bhanu Prakash Lohani, Vimal Bibhu, Ajit Singh

10.14445/23488387/IJCSE-V4I6P101

How to Cite?

Bhanu Prakash Lohani, Vimal Bibhu, Ajit Singh, "Review of Evolutionary Algorithms based on parallel computing paradigm," SSRG International Journal of Computer Science and Engineering , vol. 4,  no. 6, pp. 1-4, 2017. Crossref, https://doi.org/10.14445/23488387/IJCSE-V4I6P101

Abstract:

Evolutionary algorithms are used to find the optimal solution for the real world problem based upon the concept of biological evolution. When the data size is very large and fitness function is complex in nature at that time sequential evolutionary algorithms fails to give satisfactory result in the given time domain. Hence the parallelization is done to that can handle the complex real world optimization problems in the reasonable time. Parallelization is a process which is used to do speed up to the existing system to provide the result in the required time frame. In this paper we have presented the review of various evolutionary algorithms studied for the literature review for the research work. We had also summarized Why parallelization is needed for any evolutionary algorithm how we can implement the parallelization with the help of Hadoop Map reduce architecture. We have also presented a comparative result analysis of Particle swarm optimization algorithm. The parallel version of Particle swarm optimization is implemented with the help of Hadoop MR Architecture.

Keywords:

Evolutionary algorithm, Swarm, Parallelization, optimization, Hadoop, biological evolution, mutation.

References:

[1] Yue-Jiao, Wei-Neng Chen, Zhi-Hui Zhan, Jun Zhang, Yun Li, and Qingfu Zhang. "Distributed evolutionary algorithms and their models: A survey of the state-of-the-art." Applied Soft Computing (2015).
[2] McNabb, Andrew W., Christopher K. Monson, and Kevin D. Seppi. "Parallel pso using mapreduce." In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pp. 7-14. IEEE, 2007.
[3] B. Bullnheimer, G. Kotsis, and C. Strauss. Parallelizati on Strategies for the Ant System. Technical Report POM 9/97, University of Vienna, 1997.
[4] K.S.Tang, K.F. Man,S.Kwong and P.J. Fleming, “GA Approach to Multiple Objective Optimization for Active Noise Control,”Algorithms and Architecturesfor Real-Time Control 95,pp. 13-19, Belgium,31 May-2 Jun 1995.
[5] Yang, Xin-She, and Suash Deb. "Engineering optimization by cuckoo search." International Journal of Mathematical Modeling and Numerical Optimization 1, no. 4 (2010): 330-343.
[6] Venkata Vijaya Geeta. Pentapalli,Ravi Kiran Varma P"Cuckoo Search Optimizationand its Applications: A Review"Vol. 5, Issue 11, November 2016.
[7] Artificial Bee Colony (Abc), Harmony Search And Bees Algorithms On Numerical Optimization, D.Karaboga, B. Akay,Erciyes University, The Dept. Of ComputerEngineering, 38039, Melikgazi, Kayseri, Turkiye.
[8] del Río, Sara, Victoria López, José Manuel Benítez, and Francisco Herrera. "On the use of MapReduce for imbalanced big data using Random Forest." Information Sciences 285 (2014): 112-137.
[9] Alham, Nasullah Khalid, Maozhen Li, Yang Liu, and Suhel Hammoud. "A MapReduce-based distributed SVM algorithm for automatic image annotation." Computers & Mathematics with Applications 62, no. 7 (2011): 2801-2811.
[10] Wu, Xindong, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. "Data mining with big data." Knowledge and Data Engineering, IEEE Transactions on 26, no. 1 (2014): 97-107.
[11] Chen, CL Philip, and Chun-Yang Zhang. "Data-intensive applications, challenges, techniques and technologies: A survey on Big Data." Information Sciences 275 (2014): 314-347.
[12] Xu, Xingjian, Zhaohua Ji, Fangfang Yuan, and Xiaoqin Liu. "A Novel Parallel Approach of Cuckoo Search using MapReduce." In 2014 International Conference on Computer, Communications and Information Technology (CCIT 2014). Atlantis Press, 2014.
[13] Moraes, Antonio OS, et al. "A robust parallel algorithm of the particle swarm optimization method for large dimensional engineering problems." Applied Mathematical Modelling 39.14 (2015): 4223-4241.
[14] He, Yaobin, Haoyu Tan, Wuman Luo, Huajian Mao, Di Ma, Shengzhong Feng, and Jianping Fan. "Mr-dbscan: An efficient parallel density-based clustering algorithm using MapReduce." In Parallel and Distributed Systems (ICPADS), 2011 IEEE 17th International Conference on, pp. 473-480. IEEE, 2011.