A Survey on Metaheuristics for Solving Large Scale Optimization Problems

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 5
Year of Publication: 2017
Authors:
Atinesh Singh, Nanda Dulal Jana
10.5120/ijca2017914839

Atinesh Singh and Nanda Dulal Jana. A Survey on Metaheuristics for Solving Large Scale Optimization Problems. International Journal of Computer Applications 170(5):1-7, July 2017. BibTeX

@article{10.5120/ijca2017914839,
	author = {Atinesh Singh and Nanda Dulal Jana},
	title = {A Survey on Metaheuristics for Solving Large Scale Optimization Problems},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {5},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume170/number5/28063-2017914839},
	doi = {10.5120/ijca2017914839},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In recent years, there has been a remarkable improvement in the computing power of computers. As a result, numerous realworld optimization problems in science and engineering, possessing very high dimensions, have appeared. In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems. Broadly these algorithms can be categorized in 3 groups: Standard Evolutionary Algorithms, Cooperative Co-evolution (CC) based Evolutionary Algorithms and Memetic Algorithms. This paper gives a brief introduction of some state-of-the-art Metaheuristics used in the field of LSGO, discusses their performance in CEC Competition on LSGO and finally, future scope in this field is presented.

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Keywords

Evolutionary Computation, Large Scale Optimization, Black-Box Optimization, Computational Intelligence