1) generalized multiobjective minimax problem
广义多目标minmax问题
1.
In this paper,the optimality conditions for the generalized multiobjective minimax problem are derived.
本文讨论了广义多目标minmax问题的最优性条件。
2) generalized minmax problem
广义 minmax 问题
1.
For the generalized minmax problem,we establish a rigorous duality relationship between entropy regularization and exponential penalty methods,with an attempt to a bridge between entropy optimization and conventional optimization methods.
本文对广义 minmax 问题,建立了熵正则化方法和指数罚函数法之间的严格对偶关系。
3) minmax problem
minmax问题
1.
The constrained optimization problem is transformed to unconstrained optimization problem and minmax problem respectively through penalty function.
本文以约束优化问题为对象,首先介绍了采用罚函数法将约束优化问题化为无约束优化问题,和将约束优化问题转化为minmax问题,然后对无约束优化问题和minmax问题,采用PSO算法进行进化求解;在此基础上,以目标函数和约束满足分别为优化目标提出了一种双微粒群的PSO算法。
5) multi-objective problem
多目标问题
1.
This paper studies hybrid multiple attribute decision making problem with real numbers and interval numbers and triangle fuzzy numbers,and an unknown weight information on indexes,and affiliate degree model is put forward,then the multi-objective problem is transformed into single-objective problem,and the ranking of alternatives is expressed by affiliate degree.
研究了指标权重未知,指标值为实数、区间数、三角模糊数三种数据类型同时存在的混合型多属性决策问题,提出了从属度方法,把多目标问题转化为单目标问题,根据从属度大小对各方案进行排序。
2.
This paper discusses uncertain multiple attribute decision making problem with real numbers and interval numbers and triangle fuzzy numbers, and affiliate degree model is put forward, then the multi-objective problem is transformed into single-objective problem, and the ranking of alternatives is expressed by affiliate degree.
本文讨论了属性值为实数、区间数、三角模糊数三种数据类型的混合型多属性决策问题,提出了从属度方法,把多目标问题转化为单目标问题,利用从属度对各方案进行捧序。
6) multi-objective optimal problem
多目标优化问题
1.
The new representation proposed in this paper deals with the partitional clustering problem by regarding it as a multi-objective optimal problem;in this approach the niche Pareto genetic algorithm is used to solve the problem.
为了解决该问题,提出将产状数据的划分问题转化为多目标优化问题,并采用小生境Pareto遗传算法进行求解。
2.
This paper introduce a new and efficient multi-objective evolutionary algorithms (EMOEA) for solving unconstrained and constrained multi-objective optimal problems (MOPs).
利用 Better函数对多目标优化问题 (MOP)建立了一种偏序关系 ,并通过这种偏序关系对种群中的个体进行排名 ,设计了一种高效的多目标演化算法 (EMOEA) ,应用有限 Markov链的有关理论证明了此演化算法的收敛性 ,同时对四个 Benchmark函数进行了数值试验 ,测试结果表明 ,新算法在解集分布的均匀性、多样性、精确性以及快速收敛性均优于已有的 MOEA。
补充资料：广义特征值问题数值解法
见代数特征值问题数值解法。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条