A Hybrid Multi-objective Optimization Algorithm Based on NSGA-II and MOGWO and Its Application to Optimal Design of Electromagnetic Devices

Due to the increasingly fierce competition in science, technology and economy, optimization algorithms play a crucial role in the design of electromagnetic devices. At present, optimization methods based on evolutionary algorithms are widely used in social development, but many algorithms still reflect the problems of insufficient global search ability, easy to fall into local optimal solutions, and insufficient diversity of solutions when dealing with objective functions with multi-modal landscape, discontinuities and other characteristics. A new hybrid multi-objective optimization algorithm is proposed to solve the above problem, which is based on Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Grey Wolf Optimizer (MOGWO) algorithm. In this new optimization algorithm, NSGA-II is chosen as the core, and leader decision-making mechanism of MOGWO and other techniques are adopted, which improves the convergence performance of the hybrid algorithm, and enhances the diversity of the Pareto solutions. Performance testing of the new hybrid algorithm for convergence and diversity, and it is applied to the optimal design problem of electromagnetic devices. The results obtained demonstrate that the algorithm has correctness and effectiveness.