Article

Biogeography-based optimization and the solution of the power flow problem

Authors:

F. Eugenio Villaseca,

Bharat VyakaranamAuthors Info & Claims

SMC'09: Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

Pages 1003 - 1008

Published: 11 October 2009 Publication History

Abstract

Biogeography-based optimization (BBO) is a novel evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the study of the geographical distribution of biological organisms. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. This paper presents an application of the BBO algorithm to the power flow problem for an IEEE 30-bus Test Case system. The BBO solution is compared with the solution of the same problem using a genetic algorithm (GA). The results of Monte Carlo simulations indicate that the BBO algorithm consistently performs better than the GA in determining an optimal solution to the power flow problem.

References

[1]

L. Lai and N. Sinha, "Genetic algorithms for solving optimal power flow problems," Chapter 17, Modern Heuristic Optimization Techniques, pp. 471-500, IEEE Press, 2008.

[2]

A. Wallace, The Geographical Distribution of Animals, first published in 1876, Adamant Media Corporation, 2006.

[3]

C. Darwin, The Origin of Species, first published in 1859, Gramercy, 1995.

[4]

E. Munroe, "The geographical distribution of butterflies in the West Indies," PhD Dissertation, Cornell University, Ithaca, New York, 1948.

[5]

R. MacArthur and E. Wilson, The Theory of Biogeography, Princeton University Press, 1967.

[6]

D. Simon, "Biogeography-based optimization," IEEE Transactions on Evolutionary Computation Vol. 12, No. 6, pp. 702-713, December 2008.

Digital Library

[7]

J. Carpentier, "Contribution to the study of economic dispatch," Bulletin of the French Society of Electricians, Vol. 3, No. 8, pp. 431-437, 1962.

[8]

A. Wood and B. Wollenberg, Power Generation Operation and Control, Wiley-Interscience, NJ, 1996.

[9]

D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, MA, 1989.

Digital Library

[10]

J. Yuryevich and K. Wong, "Evolutionary programming based optimal power flow algorithm," IEEE Transactions on Power Systems, Vol. 14, No. 4, pp. 1245-1250, November 1999.

[11]

S. Agrawal, B. Panigrahi, and M. Tiwari, "Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch," IEEE Transactions on Evolutionary Computation, Vol. 12, No. 5, pp. 529-541, October 2008.

Digital Library

[12]

K. Lenin and M. Mohan, "Ant colony search algorithm for optimal reactive power optimization," Serbian Journal of Electrical Engineering Vol. 3, No. 1, pp. 77-88, June 2006.

[13]

J. Glover and S. Sarma, Power System Analysis and Design, Edition, CL-Engineering, 2007.

Digital Library

[14]

K. Wong, A. Li, and M. Law, "Development of constrained genetic algorithm load flow method," IEE Proceedings-Generation, Transmission, and Distribution, Vol. 144, No. 2, pp. 91-99, Mar 1997.

[15]

J. Grainger and W. Stevenson, Power System Analysis, McGraw-Hill Science, NY, 1994.

[16]

R. Christie, Power Systems Test Case Archive, College of Engineering, University of Washington, WA (Dec. 2008). {Online}. Available: www.ee.washington.edu/research/pstca

[17]

H. Pierce, Jr., "Common format for exchange of solved load flow data," IEEE Transactions on Power Apparatus and Systems, Vol. PAS-92, No. 6, pp. 1916-1925, Nov/Dec 1973.

[18]

D. Simon, M. Ergezer, and D. Du, "Markov analysis of biogeographybased optimization algorithms," unpublished (Mar. 2009). {Online}. Available: http://academic.csuohio.edu/simond/bbo/markov

[19]

J. Kennedy and R. Eberhart, Swarm Intelligence, Academic Press, CA,

Digital Library

[20]

S. Rahnamayan, H. Tizhoosh, M. Salama, "Opposition-based differential evolution," IEEE Transactions on Evolutionary Computation Vol. 12, No. 1, pp. 64-79, Feb. 2008.

Digital Library

[21]

M. Ergezer, D. Simon, and D. Du, "Opposition biogeography-based optimization, unpublished.

[22]

D. Simon, "A probabilistic analysis of a simplified biogeography-based optimization algorithm," unpublished (Mar. 2009). {Online}. Available: http://academic.csuohio.edu/simond/bbo/simplified

Cited By

Yao WZhou XZhang LYang MYan S(2019)A Biogeography-Based Optimization Algorithm for Criminisi AlgorithmProceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing10.1145/3330393.3330402(25-30)Online publication date: 10-May-2019
https://dl.acm.org/doi/10.1145/3330393.3330402
Farswan PBansal J(2019)Fireworks-inspired biogeography-based optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3351-223:16(7091-7115)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00500-018-3351-2
Ma HFei MYang Z(2016)Biogeography-based optimization for identifying promising compounds in chemical processNeurocomputing10.1016/j.neucom.2015.05.125174:PA(494-499)Online publication date: 22-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.05.125
Show More Cited By

Biogeography-based optimization and the solution of the power flow problem
1. Applied computing
  1. Life and medical sciences
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic reasoning algorithms

Recommendations

Variations of biogeography-based optimization and Markov analysis

Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and ...
Statistical mechanics approximation of biogeography-based optimization

Biogeography-based optimization BBO is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from ...
Population distributions in biogeography-based optimization algorithms with elitism
SMC'09: Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

Biogeography-based optimization (BBO) is an evolutionary algorithm that is based on the science of biogeography. Biogeography is the study of the geographical distribution of organisms. In BBO, problem solutions are represented as islands, and the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

SMC'09: Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

October 2009

5232 pages

ISBN:9781424427932

Publisher

IEEE Press

Publication History

Published: 11 October 2009

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yao WZhou XZhang LYang MYan S(2019)A Biogeography-Based Optimization Algorithm for Criminisi AlgorithmProceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing10.1145/3330393.3330402(25-30)Online publication date: 10-May-2019
https://dl.acm.org/doi/10.1145/3330393.3330402
Farswan PBansal J(2019)Fireworks-inspired biogeography-based optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3351-223:16(7091-7115)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00500-018-3351-2
Ma HFei MYang Z(2016)Biogeography-based optimization for identifying promising compounds in chemical processNeurocomputing10.1016/j.neucom.2015.05.125174:PA(494-499)Online publication date: 22-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.05.125
Golafshani EAshour A(2016)A feasibility study of BBP for predicting shear capacity of FRP reinforced concrete beams without stirrupsAdvances in Engineering Software10.1016/j.advengsoft.2016.02.00797:C(29-39)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.advengsoft.2016.02.007
Guo WChen MWang LWu Q(2016)Backtracking biogeography-based optimization for numerical optimization and mechanical design problemsApplied Intelligence10.1007/s10489-015-0732-444:4(894-903)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10489-015-0732-4
Golafshani E(2015)Introduction of Biogeography-Based Programming as a new algorithm for solving problemsApplied Mathematics and Computation10.1016/j.amc.2015.08.026270:C(1-12)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.amc.2015.08.026
García-Torres JDamas SCordón OSantamaría J(2014)A case study of innovative population-based algorithms in 3D modelingExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.08.07441:4(1750-1762)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1016/j.eswa.2013.08.074
Qu ZMo H(2011)Research of hybrid biogeography based optimization and clonal selection algorithm for numerical optimizationProceedings of the Second international conference on Advances in swarm intelligence - Volume Part I10.5555/2026282.2026335(390-399)Online publication date: 12-Jun-2011
https://dl.acm.org/doi/10.5555/2026282.2026335
Thomas GLozovyy PSimon D(2011)Fuzzy robot controller tuning with biogeography-based optimizationProceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II10.5555/2025816.2025849(319-327)Online publication date: 28-Jun-2011
https://dl.acm.org/doi/10.5555/2025816.2025849
Scheidegger CShah ASimon D(2011)Distributed learning with biogeography-based optimizationProceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II10.5555/2025816.2025837(203-215)Online publication date: 28-Jun-2011
https://dl.acm.org/doi/10.5555/2025816.2025837
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents