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Dr. Mohamed Shibl Albags :: Publications:

Title:
Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem
Authors: Wael Taha Elsayed, Graduate Student Member, IEEE, Yasser G. Hegazy, Senior Member, IEEE, Mohamed S. El-bages, and Fahmy M. Bendary
Year: 2017
Keywords: Economic dispatch (ED) problem, metaheuristic technique, random drift particle swarm optimization (RDPSO), valve point effects.
Journal: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume: 13
Issue: 2
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper mohamed shibl albags_paper5.docx
Supplementary materials Not Available
Abstract:

This paper proposes an improved version of the random drift particle swarm optimization algorithm for solving the economic dispatch problem. The improvement is achieved through adding a crossover operation followed by a greedy selection process while replacing the mean best position of the particles with the personal best position of each particle in the velocity updating equation. The improved algorithm is also augmented with a self-adaption mechanism that eliminates the need for tuning the algorithm parameters based on characteristics of the considered optimization problem. Practical features such as valve point effects, prohibited operating zones, multiple fuel options, and ramp rate limits are considered in the mathematical formulation of the economic dispatch problem. In order to demonstrate the efficacy of the proposed algorithm, five benchmark test systems are utilized. The obtained results showed that the improved random drift particle swarm optimization algorithm is capable of providing superior results compared to the original algorithm and the state of the art techniques proposed in previous literature.

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