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ABSTRACT
Imperialist competitive algorithm is an evolutionary algorithm introduced for optimization problems. In this paper, multi-objective modified imperialist competitive algorithm is proposed for brushless DC motor optimization problem. In the proposed algorithm, the movement of countries toward the best imperialist is concentrated and some techniques are used to extend the single-objective algorithm to the multi-objective version. Then, the algorithm is used to optimize the design variables of brushless DC motor to maximize efficiency, minimize total mass, and satisfy six inequality constraints simultaneously. Simulation results show the superiority of the proposed algorithm over multi-objective versions of standard imperialist competitive algorithm, particle swarm optimization, improved strength Pareto evolutionary algorithm and non-dominated sorting genetic algorithm III.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
MohammadAli Sharifi
MohammalAli Sharifi was born in Iran, in 1983. He received the BSc and MSc degrees in electronics engineering from Guilan University, Iran, in 2008 and Qazvin Islamic Azad University (QIAU), Iran, in 2013, respectively. Currently, he is doing a Master's degree in mechatronics in Johannes Kepler University (JKU), Austria. His current research activities include automation, signal processing, adaptive filtering, system identification, and evolutionary algorithms.
E-mail: [email protected]
Hamed Mojallali
Hamed Mojallali was born in Iran, in 1974. He received the PhD degree in control engineering from Iran University of Science and Technology (IUST), Iran, in 2006. He is currently associate professor at Electrical Engineering Department, University of Guilan, Iran. His current research activities include modeling and system identification, evolutionary algorithms, and hybrid systems.
E-mail: [email protected]