Smith chart-based particle swarm optimization algorithm for multi-objective engineering problems

Document Type : Research Article

Authors

• A. Falloun ¹
• Y. Dursun ²
• A. Ait Madi ³

¹ dvanced Systems Engineering Laboratory, National School of Applied Sciences, Keni-tra, Morocco.

² Electrical and electronic engineering, Marmara University,Istanbul, Türkiye.

³ Advanced Systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco.

10.22067/ijnao.2024.86247.1371

Abstract

Particle swarm optimization (PSO) is a widely recognized bio-inspired algorithm for systematically exploring solution spaces and iteratively iden-tifying optimal points. Through updating local and global best solutions, PSO effectively explores the search process, enabling the discovery of the most advantageous outcomes. This study proposes a novel Smith chart-based particle swarm optimization to solve convex and nonconvex multi-objective engineering problems by representing complex plane values in a polar coordinate system. The main contribution of this paper lies in the utilization of the Smith chart’s impedance and admittance circles to dynamically update the location of each particle, thereby effectively deter-mining the local best particle. The proposed method is applied to three test functions with different behaviors, namely concave, convex, noncon-tinuous, and nonconvex, and performance parameters are examined. The simulation results show that the proposed strategy offers successful conver-gence performance for multi-objective optimization applications and meets performance expectations with a well-distributed solution set.

Keywords

• Multi-objective optimization (MOO)
• particle swarm optimiza-tion (PSO)
• meta-heuristic optimization

Main Subjects

• Control and Optimization

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