Application and optimization design of improved multi-objective particle swarm

An improved multi-objective particle swarm optimization(MOPSO)is presented in the paper.To overcome the shortcoming of particle swarm optimization(PSO) algorithm, that plunging into the local mimimum,an advanced PSO algorithm with mutation operator is introduced.Non-dominated sorting and dynamic aggregate method are used to guide the flight of particles and improve the diversity of the Pareto optimal solutions. Compared with other optimization algorithms, the proposed method is simple and fast.Use of MOPSO for designing multilayered electromagnetic absorbers and finding optimal Pareto front is described.The achieved Pareto presents optimal trade off between thickness and reflection coefficient of absorbers.The reflection coefficient function and thickness function are used to test the performance of the proposed algorithm.Simulation results show that the algorithm can converge to the global optimal with good accuracy while keeps the diversity of the Pareto solutions.