中文摘要：

将多目标函数优化问题转化成单目标约束优化问题．对转化后的问题提出了基于约束主导原理的选择方法，克服了多数方法只使用Pareto优胜关系作为选择策略而没有采用偏好信息这一缺陷；Memetic算法是求解多目标优化问题最有效的方法之一，它融合了局部搜索和进化计算．新的多目标Memetic算法引进C-metric，将模拟退火算法与遗传算法结合起来，改善了全局搜索能力．用概率论的有关知识证明了算法的收敛性．仿真结果表明该方法对不同的试验函数均可求出一组沿着Pareto前沿分布均匀且散布广泛的非劣解．

英文摘要：

The multi-objective optimization problem is converted into a constrained optimization problem. Based on the constraint dominance principle, a new selection strategy is proposed for the converted problem to remove the drawback in most algorithms taking Pareto dominance as selection strategy but ignoring preference information. Memetic algorithm is one of the most efficient algorithms for optimizing multi-objective problems, incorporating local search into evolutionary computation. The new multi-objective Memetic algorithm combines the genetic algorithm with simulated annealing algorithm by introducing the C-metric to improve the global search ability. The convergence of this algorithm is proved with related theories of probability. Simulation results demonstrate the ability of the new algorithm in finding the uniformly distributed and widely-spread non-trivial solutions on the entire Pareto front.