中文摘要：

如何使算法快速收敛到真正的Pareto前沿，并保持解集在前沿分布的均匀性是多目标优化算法重点研究解决的问题。该文提出一种基于量子遗传算法的多目标优化算法，利用量子遗传算法的高效全局搜索能力，在整个解空间内快速搜索多目标函数的Pareto最优解，利用量子遗传算法维持解集多样性的特点，使搜索到的Pareto最优解在前沿均匀分布。通过求解带约束的多目标函数优化问题，对该文算法的多目标优化性能进行了考察，并与NSGAII，PAES，MOPSO和Ray-Tai-Seow’s算法等知名多目标优化算法进行比较，结果证明了该文算法的有效性和先进性。

英文摘要：

How causes the algorithm fast to restrain to the true Pareto optimal front, and maintains solutions distributed uniformly in the Pareto optimal front is one of the key research issue. A multi-objective optimization algorithm is proposed based on Q-bit Coding Genetic Algorithm （QCGA）. By right of the capability of efficient global search and maintenance of diversity of QGA, it explores the feasible region for Pareto optimal solutions quickly and maintains the solutions distributed uniformly over the Pareto optimal front, Characteristics of the algorithm are confirmed through optimization experiments of multi-objective functions with constraints, Compared with several well-known algorithms such as NSGAII, PAES, MOPSO, experiment results prove the algorithm validity ＂and efficiency.