【摘要】 基于 Pareto最优解的定义 ,通过构造新型的联赛式选择复制等算子而发展了一种适合于求解多目标优化设计的 Pareto基因算法。通过等级法来正确识别每一代中近 Pareto波阵面的解 ,从而消除选择误差达到快速收敛的目的。为提高解的分布性 :采用小生境技术解决了基因材料多样性损失问题 ;采用常规实数编码方式配合平均交叉算子解决了编码端点效应问题。将所发展的方法应用于多目标翼型优化设计中 ,获得了理想的 Pareto波阵面 ,为决策者提供了一个可选的有效解数据库更多还原

【Abstract】 Based on the definition of Pareto optimum, a Pareto Genetic Algorithm (PGA) suitable for multi objective optimization is developed using a set of operators and strategies in this paper. The search process of the PGA is speeded up by presenting a ranking method. The distribution of the solutions on the Pareto front is improved from two angles. Firstly, the niching technology is used to keep the genetic diversity. Secondly, the real coding combined with an average crossover operator is used to eliminate the ending effect of the GA coding. Then the constructed PGA is used to solve the multi objective airfoil optimization problem. And the satisfying Pareto front is obtained.更多还原