A novel quantum motivated particle swarm method with enhanced search strategy for electromagnetic optimization problems

Parameter estimation of electromagnetic design problem is a complex task for traditional methods. However, quantum inspired optimization offers favorable advantages for tackling such problems. Since, many existing optimization techniques are prone to premature convergence due to an improper balanced exploration exploitation tradeoff and unstable control parameters. Therefore, to maintain a high solution quality it is necessary to overcome these challenges. In this context, this research work proposes a novel search-based quantum inspired particle swarm optimization (SQPSO) for electromagnetic applications. The proposed methodology incorporates a probability-based mutation mechanism guided by the global best solution, enabling more effective exploration of the search region. A tournament-driver selection strategy is further employed to identify superior particles and strengthen search diversity. In addition, a newly designed dynamic parameter strategy is incorporated to enhanced global search capability and improves algorithmic stability. Extensive benchmark tests and application-oriented case studies confirm that the proposed method consistently achieves faster convergence, higher-quality solutions and stronger robustness against local optima when compared with existing well-established metaheuristic techniques.