文件名称:Standard_evolutionary_algorithm_design_and_analysi
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为了有效检测多目标优化进化算法的性能，从3 个方面进行多目标优化测试问题的设计，即约束条件、最优解分布的均匀性、算
法逼近Pareto 最优前沿的难度，采用NSGA-Ⅱ算法对这些测试问题进行仿真实验，并将算法求得的最优解可视化。结果显示，测试问题能够有效检测算法在上述3 方面的性能。-In order to effectively detect the multi-objective optimization evolutionary algorithm performance, from the three aspects of multi-objective optimization test problems of design, that constraint, the uniformity of the optimal solution, Pareto optimal front approximation algorithm of the difficulty of algorithm using NSGA-Ⅱ test questions on these simulations, and obtained the optimal solution algorithm visualization. The results show that the problem can be effectively tested in the above-mentioned three aspects of detection algorithm performance.
法逼近Pareto 最优前沿的难度，采用NSGA-Ⅱ算法对这些测试问题进行仿真实验，并将算法求得的最优解可视化。结果显示，测试问题能够有效检测算法在上述3 方面的性能。-In order to effectively detect the multi-objective optimization evolutionary algorithm performance, from the three aspects of multi-objective optimization test problems of design, that constraint, the uniformity of the optimal solution, Pareto optimal front approximation algorithm of the difficulty of algorithm using NSGA-Ⅱ test questions on these simulations, and obtained the optimal solution algorithm visualization. The results show that the problem can be effectively tested in the above-mentioned three aspects of detection algorithm performance.
相关搜索： 多目标优化
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标进化算法测试问题的设计与分析.pdf