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Hybridization of Biogeography-Based: Optimization with Differential Evolution for Solving Optimal Power Flow Problems

Hybridization of Biogeography-Based: Optimization with Differential Evolution for Solving Optimal Power Flow Problems

Provas Kumar Roy, Dharmadas Mandal
Copyright: © 2013 |Volume: 2 |Issue: 3 |Pages: 16
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781466634541|DOI: 10.4018/ijeoe.2013070106
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MLA

Roy, Provas Kumar, and Dharmadas Mandal. "Hybridization of Biogeography-Based: Optimization with Differential Evolution for Solving Optimal Power Flow Problems." IJEOE vol.2, no.3 2013: pp.86-101. http://doi.org/10.4018/ijeoe.2013070106

APA

Roy, P. K. & Mandal, D. (2013). Hybridization of Biogeography-Based: Optimization with Differential Evolution for Solving Optimal Power Flow Problems. International Journal of Energy Optimization and Engineering (IJEOE), 2(3), 86-101. http://doi.org/10.4018/ijeoe.2013070106

Chicago

Roy, Provas Kumar, and Dharmadas Mandal. "Hybridization of Biogeography-Based: Optimization with Differential Evolution for Solving Optimal Power Flow Problems," International Journal of Energy Optimization and Engineering (IJEOE) 2, no.3: 86-101. http://doi.org/10.4018/ijeoe.2013070106

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Abstract

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

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