智能优化

面向FlowShop的智能优化调度系统

An Intelligent Optimization System for Flow Shop Scheduling Problem

智能优化算法在光学CAD系统中的应用

The Intelligent Optimization Algorithms in Optics CAD System

采用改进的离散变量遗传算法实现了该智能优化控制设计，并运用C语言编制了一般遗传算法和改进的遗传算法优化程序。

The programs of genetics algorithms and the improved genetics algorithm are developed with C.

基于智能优化PID的矢量控制系统

The Vector Control System Based on Ant Colony Optimization

专家系统及机械CAD中的智能优化方法

A solution to the Best-Choice of Artificial Intelligence in the Expert System and the Mechanical CAD

本文通过引入异步进化策略设计与改进现存的智能优化算法用于解决FlowShop调度问题。

This paper presented a new evolution strategy and a new improved intelligent algorithm to solve the flow shop scheduling problem .

网络流量预测中基于群智能优化的SVM模型

A Support Vector Machine Model Based on Swarm Algorithm Optimization in Network Traffic Prediction

群智能优化算法PSO及其在几类模型优化中的应用

Intelligent Group Optimization Algorithm PSO and Its Application in Several Types Models Optimization

BF型柔性传动改进设计及智能优化算法程序开发

The Improvement Design of BF Flexible Transmission and the Program Development of Intelligent Optimization Algorithms

在此基础上，构建了基于群体智能优化手段的预测神经网络，并对传统的BP神经网络进行了优化。

On this basis , the existing neural network to improve and build intelligent optimization methods based on multilayer feed forward neural network .

人工萤火虫群优化（GlowwormSwarmOptimization，GSO）算法是一种新的群智能优化算法。

Artificial Glowworm Swarm Optimization ( GSO ) Algorithm is a new swarm intelligence algorithm .

智能优化算法在解决大规模的NP-hard优化问题，有着不可比拟的优越性。

Intelligent optimization algorithms have incomparable superiority in solving large-scale NP-hard optimization problems .

欧氏Steiner最小树问题的智能优化算法

Intelligent Optimization Algorithms for Euclidean Steiner Minimum Tree Problem

差分进化算法（differentialevolution，DE）作为一种新兴的群体智能优化算法，因其具有较强的全局寻优能力和较快的寻优速率，逐渐成为研究的热点。

As a new population-based intelligent optimization algorithm , because of its good global search and quick convergence ability , differential evolution ( DE ) has gradually become a research hotspot .

由于NP问题求解的复杂性，目前车辆路径问题的求解方法主要使用各种智能优化算法。

Due to the complexity of the problem solving NP vehicle routing problem solving method using a variety of intelligent optimization algorithm .

粒子群优化算法（ParticleSwarmoptimization，PSO算法）源于鸟群和鱼群群体运动行为的研究，是一种新的群体智能优化算法，是演化计算领域中的一个新的分支。

Particle swarm optimization ( PSO ) is an evolutionary computation technique developed by Dr. Eberhart and Dr. Kennedy in 1995 , inspired by social behavior of bird flocking or fish schooling .

模拟退火算法是目前发展较快的智能优化算法，是一种以概率l收敛于全局最优解的全局优化算法。

Simulated annealing is an intelligent algorithm of developing very fast .

其中重点介绍了摩托罗拉智能优化工具（IOS）及用户感受测试工具（Angel）应用的优势。

Motorola , which focuses on the intelligent optimization tools ( IOS ) and user experience testing tools ( Angel ) the advantages of the application .

智能优化模型和人机接口通过VISUALBASIC6.0实现。智能优化系统和PLC控制系统之间的数据交换通过OPC技术实现。

The model of the intelligent optimizing system and HMI is implemented by the Visual Basic 6.0 . The data exchange between the intelligent optimizing system and PLC control system with the OPC technology .

最后，本文将所采用的智能优化算法与基于Know-how知识的人工负荷分配算法进行了比较，验证了算法的有效性。

At last , this dissertation verifies the efficienty of those algorithms , by comparing these algorithms with the artificial algorithm based on the Know-how knowledge .

其次，智能优化算法对Pareto最优前端的形状和连续性不敏感，能很好地逼近非凸或不连续的最优前端。

Secondly , the intelligent optimization algorithm for Pareto optimal front end shape and continuity is not sensitive , can be very good approximation of convex or discontinuous optimal front .

基于近些年出现的新型智能优化思想：人工蚂蚁系统，给出了一种可快速求解VRP的蚂蚁搜索算法。

Based on the recently developed new intelligent optimization idea : artificial ant system , we proposes a quick ant searching algorithm for solving VRP .

以La、Ce、Pr、Nd四组分串级萃取分离Ce/Pr稀土分离过程为研究对象，开发了稀土串级萃取分离过程智能优化控制仿真系统。

A certain Ce / Pr extraction separation production line of La , Ce , Pr , Nd tetra-component system was considered and a simulation system for intelligent optimal control of rare earth cascade extraction separation process was developed .

本文通过采用智能优化遗传算法（GA）和基于群集智能的蚁群算法（ACO）对AS/RS的若干优化问题进行研究，提出了相应的改进算法并进行了实例验证。

This dissertation investigates some optimization problems of AS / RS based on Genetic Algorithm ( GA ) and Ant Colony Algorithm ( ACO ), proposes corresponding improved algorithms and carries out to validate the algorithms with a practical example .

由于NP完全问题无法用多项式算法解决，许多智能优化算法得以发展并用于求解TSP问题，例如模拟退火算法、遗传算法和神经网络算法。

Since there is no polynomial time algorithm able to solve NP-complete problems , many intelligent optimization algorithms have been developed in order to achieve a solution to the TSP , such as simulated annealing algorithm , genetic algorithm and neural network .

在通用CAE软件平台（MSC.PATRAN）上通过二次开发编程环境实现了参数化建模，有限元分析与智能优化设计方法的集成，并设计了方便参数调试和优化设计的界面。

An integration method of intelligent optimization design , finite element analyze and parameterized modeling based on general CAE software 's second development platform ( MSC . Patran ) is constructed . An optimal design platform that is conveniently for debugging parameters is presented .

实例表明利用智能优化算法建立的燃烧优化系统可实现锅炉燃烧系统高锅炉效率、低制粉单耗、低NOx排放等多目标优化，可以达到节能减排的目的。

These results showed that the use of intelligent optimization algorithms to optimize the combustion system can achieve high efficiency , low milling unit consumption , low emissions and multi-objective optimization . It also can achieve the purpose of saving energy and reduce emissions .

第二种是基于肾上腺激素调节机制的智能优化控制器（ALIC）。

The second one is an intelligent optimized controller based on the regulation mechanism of adrenalin ( ALIC ) in endocrine system .

在回声状态神经网络的应用上通常使用专家的经验设置，这很不利于高效率的使用计算资源，本文使用了PSO的集群智能优化算法对回声状态神经网络中的参数进行了优化。

The parameters in echo state networks involved in application are set by expert of echo state networks commonly , which usually waste of computation resource , in this paper we present one method that to optimize the parameters in echo state networks by PSO optimizer when applications .

针对传统优化方法的局限性，以及遗传算法的缺陷，提出了一种新的智能优化方法复合遗传算法（CGA）。

With consideration of limitation of the traditional optimal methods and basic genetic algorithm , a new artificial intelligent optimal method-complex genetic algorithms is established by the way of combining complex shape method and genetic algorithms to solve this problem .