关键词: 粒子群优化算法, 小生境技术, 模拟退火算法, 多目标优化, 气动布局设计

Abstract: A new hybrid multi-objective particle swarm optimization (MOPSO) is built in order to improve the searching efficiency and keep the diversity of noninferior set. Pareto operator, niche technique and simulated annealing method are incorporated into the new hybrid MOPSO. The algorithm is applied to the multi-objective optimization of aerodynamic configuration design of a re-entry hypersonic vehicle with respect to lift-to-drag ratio and volumetric efficiency given the constraint of pitching stability. The optimal solutions are compared with those generated by the basic MOPSO. It is shown that the new hybrid MOPSO can produce a robust and well distributed noninferior set for complex multi-objective design projects, which can help designers understand their design project and make decisions.

Key words: particle swarm optimization, niche technique, simulated annealing method, multi-objective optimization, aerodynamic configuration design