International Journal of Computational Intelligence Systems

Volume 9, Issue 4, August 2016, Pages 652 - 665

Modified Black Hole Algorithm with Genetic Operators

Authors
Saber Yaghoobisaber.yaghoobi@gmail.com, Hamed Mojallalimojallali@guilan.ac.ir
Electrical Engineering Department, Faculty of Engineering,University of Guilan, Rasht, PO Box 3756, Guilan Province, Iran
Received 3 November 2015, Accepted 12 March 2016, Available Online 1 August 2016.
DOI
10.1080/18756891.2016.1204114How to use a DOI?
Keywords
Black Hole; Nature-inspired optimization; Metaheuristic algorithm; Benchmarking
Abstract

In this paper, a modified version of nature-inspired optimization algorithm called Black Hole has been proposed. The proposed algorithm is population based and consists of genetic algorithm operators in order to improve optimization results. The proposed method enhances Black Hole algorithm performance by searching space with more diversity. The modified Black Hole algorithm has been applied to a well-known benchmark. The experimental results show that the modified Black Hole algorithm outperforms compared to some prominent optimization algorithms.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 4
Pages
652 - 665
Publication Date
2016/08/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1204114How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Saber Yaghoobi
AU  - Hamed Mojallali
PY  - 2016
DA  - 2016/08/01
TI  - Modified Black Hole Algorithm with Genetic Operators
JO  - International Journal of Computational Intelligence Systems
SP  - 652
EP  - 665
VL  - 9
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1204114
DO  - 10.1080/18756891.2016.1204114
ID  - Yaghoobi2016
ER  -