Paper Title
Hybridized QPSO for Electromagnetic Design Problems USINGDE Technique with Dynamic Parameters

The quantum particle swarm optimization (QPSO) method of swarm intelligence has successfully resolved several electromagnetic inverse problems. The approach encounters local minima and lost diversity in the final stages of optimization. To address this kind of problem, a new hybridization method is presented in this paper that integrates QPSO with differential evolution (DE). The proposed HQPSODE introduces additional capabilities, such as the non-linear adaptive control parameter, crossover, and DE selection strategy are introduced to the smart QPSO approach to enhance the exploration. According to experiment results, the hybrid HQPSODE strategy has a search accuracy and convergence advantage over other optimization techniques. Keywords - Smart Particle, Hybridization, QPSO, DE, Energy Storage Device (SMES)