多目标优化问题的遗传算法改进研究

基于Nash均衡的思想在NSGA所求得的Pareto最优解基础上，探讨一种能对多目标优化问题进行求解的遗传算法。采用Nash均衡的思想在多目标优化的遗传算法，结合NSGA算法，提出一种能得到多个Pareto最优解的多目标优化算法。通过目标函数线性加权法、NSGA对函数进行了试验分析，对部分自变量进行固定，对其他的自变量进行优化，对Pareto最优解进行持续优化，进而实现加速算法的收敛，从实验中得出了这种算法具有较快的收敛性，但是其运行时间和NSGA相比没有多少改善。

Improvement of genetic algorithm for multi-objective optimization problem

Purpose of this paper the idea of Nash equilibrium based on NSGA Pareto optimal solutions are obtained based on the right to explore a multi-objective optimization problem can be solved genetic algorithms. Methods idea of Nash equilibrium in a multi-objective optimization genetic algorithm, combined with NSGA algorithm is proposed to get multiple Pareto optimal solution of multi-objective optimization algorithm. Results The linear weighted objective function method, NSGA tested for function analysis, on the part of the independent variable and fixed, for other independent variables are optimized for Pareto optimal solution for continuous optimization, thus achieving accelerated convergence of the algorithm, the conclusion from the experimental derive this algorithm has faster convergence, but its running time and NSGA little improvement compared.