Optimizing Multi-Modal Electromagnetic Design Problems using Quantum Particle Swarm Optimization with Differential Evolution

Many versatile and promising swarm intelligence evolutionary algorithms are being developed to solve engineering optimization problems. Although have been implemented in various fields, there is still potential for enhancement the domain of complex, electromagnetic, multimodal objective To effectively address shortcomings slow convergence speed observed both smart quantum particle (QPSO) differential evolution (DE), a hybrid strategy proposed. In proposed QPSODE, apart from QPSO improving exploration as whole, more additional features such non-linear adaptive control parameter, partition apply gaussian mutation mechanism, crossover selection best using Boltzmann avoid premature introduced. Consequently, applying new design algorithm several benchmark-constrained, mostly non-convex, superconducting magnetic energy storage (SMES) electromagnetic problems shows marked performance improvement. The performances QPSODE compared with those many other widely recognized population-based optimizers. Experimental results statistical analysis Friedman test show that search accuracy advantageous over approaches.