在给定了一族参数的多目标最优化问题中，灵敏度分析是定量的分析。
In the problems of a given family of parametrized multiobjective optimization, sensitivity analysis is the quantitative analysis.
进化算法作为处理复杂函数最优化、多目标最优化问题的一种有效算法，正日益受到人们的重视。
Evolutionary algorithm is one of the most effective algorithms for hard optimization and multi-objective optimization problems, which are attached more and more importance to.
在此基础上，得到了向量目标函数既是似凸又是拟凸的多目标最优化问题的G-恰当有效解集是连通的结论。
On the conditions that vector objective function is like-convex and quasi-convex, we obtain the connectedness of G-proper efficient solution set of the multiobjective optimization problem.
进化算法作为处理复杂函数最优化、全局最优化和多目标最优化问题的一种有效算法，正日益受到人们的重视。
Evolutionary algorithms are one of the effective algorithms for hard optimization, global optimization and multiobjective optimization problems, which are attached more and more importance to.
讨论凸多目标最优化问题的严有效解，建立了拉格朗日乘子定理，并把严有效解表示为一个适当的拉格朗日函数的鞍。
We establish a Lagrange multiplier theorem for strict efficiency in convex settings and express strict points as saddle points of an appropriate Lagrangian function.
针对这一问题，建立了以起升机构占用空间最小和行星齿轮减速机构用材最少为目标的多目标最优化数学模型。
Aim at the problem, this paper establishes the mathematical model of multipurpose optimization to made hoisting mechanism to occupy minimum space and planet gear to use minimum material.
最优化也包括解决大规模，离散，非线性，多目标和全球化问题的技术。
Optimization also involves techniques for solving large-scale, discrete, nonlinear, multiobjective, and global problems.
研究了漏检情况下多传感器多目标检测中的数据关联问题，并将其描述为数学规划中组合最优化问题。
This paper studies the problem of data association in multisensor multitarget detection under the condition of leakage and describes it as combinatorial optimization in mathematical programming.
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。
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.
第五和第六章考虑多目标最优化的几个理论问题。
In Chapter 5 and 6, we study a few theoretical problems in multiobjective optimization.
第五和第六章考虑多目标最优化的几个理论问题。
In Chapter 5 and 6, we study a few theoretical problems in multiobjective optimization.
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