One advantage of multi-objective genetic optimization algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run.
多目标遗传优化算法的一个优点就是可在一次迭代计算中寻找到问题的多个非劣最优解。
As a result, this method returns not a single non-dominated solution but a set of no-dominated solutions, which provides powerful decision support to the decision-maker.
通过返回一个非支配解的集合而非单一的一个非支配解，为决策者提供了更有力的决策支持。
The simulations prove that the non-dominated Pareto optimal solutions have better distribution and faster convergence at the same time in typical functions.
在典型的测试函数集上的数值实验结果表明，根据这些策略改进的算法得到的非劣解集具有较好的分布性，同时收敛速度更快。
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