电波科学学报2011，Vol.26Issue(4)：789-795,7.
改进的多目标粒子群算法优化设计及应用
Application and optimization design of improved multi-objective particle swarm
摘要
Abstract
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.关键词
多目标粒子群算法/变异算子/电磁吸收体/优化设计Key words
multi-objective partical swarm/mutation operator/electromagnetic absorber/optimization design分类
信息技术与安全科学引用本文复制引用
张兰勇,刘胜,于大泳..改进的多目标粒子群算法优化设计及应用[J].电波科学学报,2011,26(4):789-795,7.基金项目
国家自然科学基金 （）
中国博士后科学基金 （）
