Novel intelligent algorithms based on bionics become more and more popular for solving dynamic optimization problems in recent years.
新型的智能仿生算法在动态优化问题中的应用也逐渐增多。
Model predictive control was applied to solve the dynamic optimization problems of the inventory in supply chain management for the demand uncertainty of supply chain under networked manufacturing.
针对网络化制造环境下供应链需求不确定的特点，采用模型预测控制技术解决供应链管理中库存的动态优化问题。
But dynamic programming is usually applied to optimization problems like the rest of this article's examples, rather than to problems like the Fibonacci problem.
但是动态编程通常被用于最优化问题(比如本文后面的示例)，而不是像斐波纳契数这样的问题。
Agile manufacturing enterprises require ERP with some new optimization problems such as supplying chain planning, dynamic alliance forming, allocation of profit and risk, and so on.
敏捷制造企业对供应链计划、动态联盟的组成与风险利润分配等多种功能的需要，带来了一系列新的优化问题。
Now, when we talked about optimization problems in dynamic programming, I said there were two things to look for.
现在，当我们讨论，动态编程中的最优化问题时，我想说有两件事需要注意。
A method for dynamic multi-objective optimization problems (DMOPs) is given.
给出了动态多目标优化问题的一种新解法。
The single optimization method is not feasible and efficient at all time. For specific problems, several integrated strategies of dynamic optimization solutions are developed in this paper.
单一的优化方法用于不同情况下的化工动态优化问题未必可行有效，为此本文针对具体情况，提出了多种解法的集成策略。
Of the problems of optimal control, the most important two optimization methods are Pontryagin minimum(or called maximum) principle and Bellman dynamic programming.
在最优控制中，最重要的两种优化方法是庞特里亚金最小值（或称最大值）原理和贝尔曼动态规划法。
In this paper, the optimization problems of two-level dynamic systems are discussed.
本文讨论两级动态系统的优化问题。
The convergence property and dynamic characteristics of the newly proposed population migration algorithm for solving global function optimization problems are analyzed by means of probability.
用概率论分析了新提出的求解函数全局优化问题的人口迁移算法的收敛性及动态特性。
The method solves the problems which caused by dynamic planning dealing with multiple constraints and large optimization problems, and improves the precision of evolution algorithm.
计算表明，该方法避免了动态规划等算法处理多约束、大型优化问题的困难，同时提高了进化算法的精度。
This algorithm avoids the dynamic programming in sub optimization problems, and improves the efficiency of the on line computations. The simulation results show the effectiveness of the new algorithm.
该算法避免了在子问题的求解中使用效率较低的规划方法，提高了线计算的效率。
This algorithm avoids the dynamic programming in sub optimization problems, and improves the efficiency of the on line computations. The simulation results show the effectiveness of the new algorithm.
该算法避免了在子问题的求解中使用效率较低的规划方法，提高了线计算的效率。
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