Evolving ant direction differential evolution for OPF with non-smooth cost functions

Authors:
K. Vaisakh;L. R. Srinivas
Affiliations:
Department of Electrical Engineering, AU College of Engineering, Andhra University, Visakhapatnam-530003, AP, India;Department of Electrical and Electronics Engineering, S.R.K.R. Engineering College, Bhimavaram-534204, AP, India
Venue:
Engineering Applications of Artificial Intelligence
Year:
2011

Citing 2
Cited 2

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A Trigonometric Mutation Operation to Differential Evolution

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A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions

Engineering Applications of Artificial Intelligence

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Abstract

In this paper, an effective and reliable algorithm, termed as evolving ant direction differential evolution (EADDE) algorithm, for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics is presented. In this method, suitable mutation operator for differential evolution (DE) is found by ant colony search. The genetic algorithm evolves the ant colony parameters and the Newton-Raphson method solves the power flow problem. The proposed algorithm has been examined on the standard IEEE 30-bus and IEEE 57-bus systems with three different objective functions. Different cases were considered to investigate the robustness of the proposed method in finding the global solution. The EADDE provides better results compared to classical DE and other methods recently reported in the literature as demonstrated by simulation results.