A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs

Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied different design problems, these exhibit deficiencies when solving complex problems with multimodal discontinuous objective functions, which is quite common optimization designs. In this paper, hybrid multi-objective algorithm based on non-dominated sorting genetic (NSGA-II) particle swarm method (MOPSO) proposed. order enhance convergence diversity performance of algorithm, new population update mechanism MOPSO introduced. Moreover, an adaptive operator involving crossover mutation presented achieve better balance between global local searches. The validated using standard test functions superconducting magnetic energy storage (SMES) device. Numerical results demonstrate effectiveness superiority proposed method.