Biogeography-based Optimization and the Solution of the Power Flow Problem

Rick Rarick; Daniel J. Simon; F. Eugenio Villaseca; B. Vyakaranam

doi:10.1109/ICSMC.2009.5346046

Biogeography-based Optimization and the Solution of the Power Flow Problem

Rick Rarick
, Daniel J. Simon
, F. Eugenio Villaseca
, B. Vyakaranam

Cleveland State University

Research output: Other contribution

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.

Original language	American English
DOIs	https://doi.org/10.1109/ICSMC.2009.5346046
State	Published - Oct 11 2009

Keywords

Biogeography
Evolutionary computation
Genetic algorithms
Linear matrix inequalities
Load flow
Mathematical model
Nonlinear equations
Power generation
Sparse matrices
Biogeography-based optimization
power flow problem
evolutionary algorithms
power systems

Disciplines

Electrical and Computer Engineering

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10.1109/ICSMC.2009.5346046License: CC BY

ArticleDetailsFinal published version, 45.7 KBLicense: CC BY
ArticleDetailsFinal published version, 45.7 KBLicense: CC BY

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5346046

Cite this

@misc{d55069e0a26b4f33960d6ffd4e26d7d7,

title = "Biogeography-based Optimization and the Solution of the Power Flow Problem",

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.",

keywords = "Biogeography, Evolutionary computation, Genetic algorithms, Linear matrix inequalities, Load flow, Mathematical model, Nonlinear equations, Power generation, Sparse matrices, Biogeography-based optimization, power flow problem, evolutionary algorithms, power systems",

author = "Rick Rarick and Simon, \{Daniel J.\} and Villaseca, \{F. Eugenio\} and B. Vyakaranam",

note = "Rarick, R., Simon, D., Villaseca, F. E., Vyakaranam, B. Biogeography-based optimization and the solution of the power flow problem in 2009 IEEE International Conference on Systems, Man and Cybernetics, 1003 - 1008.",

year = "2009",

month = oct,

day = "11",

doi = "10.1109/ICSMC.2009.5346046",

language = "American English",

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T1 - Biogeography-based Optimization and the Solution of the Power Flow Problem

AU - Rarick, Rick

AU - Simon, Daniel J.

AU - Villaseca, F. Eugenio

AU - Vyakaranam, B.

N1 - Rarick, R., Simon, D., Villaseca, F. E., Vyakaranam, B. Biogeography-based optimization and the solution of the power flow problem in 2009 IEEE International Conference on Systems, Man and Cybernetics, 1003 - 1008.

PY - 2009/10/11

Y1 - 2009/10/11

N2 - 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.

AB - 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.

KW - Biogeography

KW - Evolutionary computation

KW - Genetic algorithms

KW - Linear matrix inequalities

KW - Load flow

KW - Mathematical model

KW - Nonlinear equations

KW - Power generation

KW - Sparse matrices

KW - Biogeography-based optimization

KW - power flow problem

KW - evolutionary algorithms

KW - power systems

UR - http://engagedscholarship.csuohio.edu/enece_facpub/31

UR - https://engagedscholarship.csuohio.edu/enece_facpub/164

UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5346046

U2 - 10.1109/ICSMC.2009.5346046

DO - 10.1109/ICSMC.2009.5346046

M3 - Other contribution

ER -

Biogeography-based Optimization and the Solution of the Power Flow Problem

Abstract

Keywords

Disciplines

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Cite this