模糊线性规划饲料配方模型的建立与应用

规划技术在工业生产、交通、化工和航天等领域等得到广泛应用.作者根据动物生长发育规律特点,建立了饲料配方模糊线性规划模型.在原有工作基础上研制了新一代饲料配方优化软件.该软件较国内外其它同类系统增加了模糊线性规划优化手段.与普通线性规划和目标规划技术相比,模糊线性规划可进一步降低饲料配方成本.本文对三种模型应用结果进行了比较和分析.     

基于遗传算法的汽车平顺性时域仿真与优化

首先,建立汽车五自由度振动系统的力学模型,利用牛顿第二定律建立动力学方程,并基于IFFT法进行路面不平度的时域模拟。然后,通过ADAMS进行对比验证平顺性模型。最后,以悬架刚度阻尼为设计变量,以悬架动挠度和轮胎相对动载荷为约束函数,以座椅中心加权加速度均方根值为目标函数,建立其优化模型,利用Matlab优化工具箱中的遗传算法函数进行优化。优化结果表明,遗传算法具有很强的全局寻优能力,优化结果大大地改善了平顺性指标,提高了汽车的性能。     

基于混沌遗传算法的干线交叉口信号控制优化研究

城市道路干线交叉口交通信号的配时优化控制直接影响了整个城市的交通状况。为了提高干线交叉口信号控制的效率,以干线交叉口模型的交通信号控制问题为背景,构造了基于最小延误的常态交通条件下干线双向绿波控制模型。同时,结合混沌理论和遗传算法各自的优势,开发了混沌遗传算法。通过该算法对模型进行仿真数据求解,并与传统优化算法相比,系统延误明显减少了。结果表明,基于混沌遗传算法的控制优化方法可以提高干线交通运行的效率。     

基于遗传算法的家庭能源系统能量优化管理

家庭能源系统是能源互联网的重要组成部分,文章针对家庭能源系统在并网模式下的能量优化管理问题,综合考虑各时段优化决策之间的联系,以系统长期运行经济效益最大化为目标,建立多时段动态优化模型。考虑到模型中存在的非线性互补约束以及多时段动态规划所导致的求解难点,利用遗传算法进行计算。最后根据实测数据建立仿真算例,验证了模型和算法的有效性。     

基于改进遗传算法的配电网重构研究

为了获取配电网中支路电流并尽可能的降低系统的功率损耗,文中提出了一种基于改进遗传算法的配电网重构方法,建立了以系统功率损耗最小化为目标的配电网重构模型,结合辐射型配电网潮流计算分支电流,并对遗传算法的染色体编码,适应度函数和交叉变异模式进行了改进,避免了计算结果的早熟收敛。结合IEEE-16和IEEE-33的配电网系统进行测试,验证了所提改进遗传算法在配电网重构优化的有效性。     

运输优化模型的研究与应用

以化工产品运输为例阐述了运输优化模型,利用线性规划算法对化工产品的运输方案进行优化,直接生成全局最优的最细粒度的运输方案,保证了运输方案的科学性。在运输优化模型应用过程中,采用时间序列对单位运输成本进行预测,解决单位运输成本初次运输无法获取以及单位运输成本更新的问题。在化工销售决策系统引入运输优化模型后,提高了运输决策的科学性,大幅降低了运输方案制定的难度及化工产品的运输成本。     

稳定平台控制器参数多目标优化

利用稳定平台机械-控制联合仿真精确的系统分析能力,提出了一种稳定平台控制器多目标优化设计方法。采用稳定平台机械系统的多体动力学模型和控制系统模型作为优化目标计算的基本模型,利用Pareto多目标遗传算法对控制器参数进行优化。为了降低遗传算法的计算量,引入代理模型技术,整个优化系统在ModelCenter内集成,实现优化过程的自动化,以提高设计效率。算例结果表明,本方法优化后的稳定平台控制系统具有更好的性能指标,并能有效控制计算成本,具有一定的应用价值。     

海运集装箱空箱调运优化模型及算法研究

为了提高船公司集装箱运输效率,降低运输成本,应用线性规划的方法,以租箱成本和集装箱空箱调运费用之和最低为目标,建立了海运集装箱空箱调运问题的优化模型。利用LINGO软件能够求得线性规划问题最优解的特点,编写了求解该问题的LINGO程序代码,通过实例验证,该模型和算法可以快速有效求得最优解。     

基于遗传算法求解线性规划问题

遗传算法是模仿自然界的"优胜劣汰"原理设计的一种近似方法。本文利用遗传算法求解一类线性规划问题,并给出了遗传算法的算法,通过两个数值例子来说明了遗传算法来求解线性规划问题的有效性。     

一种基于遗传算法的语法网络搜索方法

本文以基于隐马尔科夫模型的语音识别系统为基线系统,提出了一种基于遗传算法的语法网络搜索方法,应用于语音识别系统的语法网络搜索模块。通过遗传算法解码得到的搜索结果虽然不唯一但都是满意解,提升了该模块的全局优化搜索能力。     

基于遗传算法的直觉模糊多目标规划

提出了一种基于遗传算法的直觉模糊多目标规划模型.首先,定义了目标函数和约束函数的隶属和非隶属函数;其次,通过直觉模糊"最小-最大"算子,提出直觉模糊多目标规划模型;然后,用遗传算法进行求解,使之适应线性和非线性的情况,具有通用性;最后,通过一个算例表明,直觉模糊多目标规划的性能优于模糊多目标规划.     

An integrated GA–LP approach to communication network design

In this paper we demonstrate success with an implementation of a genetic algorithm, integrated with linear programming, for solving a minimum cost network synthesis problem. The problem is formulated to include a number of practical constraints and the technique applied to moderately large networks (50 nodes). The associated linear program may be large but successful methods have been developed with very small population sizes for the genetic algorithm. This revised version was published online in June 2006 with corrections to the Cover Date.     

WLAN中合作式功率控制算法研究

在WLAN中,延迟有限,网络整体消耗能量最小化的功率控制算法研究较少。对于该问题进行建模分析,通过理论推导,将该非凸优化问题转化成广义几何规划问题。经过分析,将问题近似为线性规划,并给出全局优化算法。对于WLAN中,多个无线接入设备在一定传输时间内的信道情况进行预测,给出延迟有限能量最小化的集中式在线算法。实验结果显示,采用提出的信道预测方法,在线算法的计算结果与集中式算法结果较为接近。     

遗传算法与FDTD优化设计智能天线阵

研究了遗传算法(GA)和时域有限差分法(FDTD)结合来优化智能天线阵列。采用FDTD精确分析考虑互耦影响的智能天线阵,然后结合遗传算法以信噪比为优化基准对加权向量和天线的结构进行了一体化的优化设计,使用Fortran和MATLAB混合编程,优化设计了均匀直线阵和圆形智能天线阵。最后,通过实验天线验证了其有效性。     

An efficient communication relay placement algorithm for content‐centric wireless mesh networks

In this paper, we investigate a communication relay placement problem to optimize the network throughput in a content‐centric wireless mesh networks (WMN), in which the WMN is enhanced by including a small set of communication relays and a subset of wireless mesh routers serving as storage nodes. Specifically, we first define the communication relay placement problem in content‐centric WMNs. We then model the problem as a mathematical programming and propose a linear programming approach for calculating the achievable network throughput when the positions of communication relays are fixed. Next, to optimally placing the communication relays, we formulate an integer linear programming problem and we develop an efficient near‐optimal approximation algorithm based on linear programming relaxation. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Joint energy efficiency and spectral efficiency optimization algorithm for UDN under the restriction of interference threshold and backhaul capacity

Aiming at the scenarios which consider the constraint of backhaul capacity restriction and interference threshold in ultra-dense networks (UDN),an integer linear programming (ILP) and Lagrangian dual decomposition (LDD) based joint optimization algorithm of energy efficiency and spectrum efficiency was proposed.In the proposed algorithms,the user association problem with the constraint of limited backhaul capacity was modelled as an ILP problem and then finished the connection between the user and the base station of microcell by solving this problem with dynamic programming method.Therefor,Lagrangian dual decomposition (LDD) was applied in an iteration algorithm for spectrum resource allocation and power allocation.The simulation results show that compared with traditional schemes,the proposed algorithm can significantly improve the energy efficiency and spectrum efficiency of system and use the microcell’s load capacity more efficiently.     

Joint Optimization for Congestion Avoidance in Cognitive Radio WMNs under SINR Model

Due to limited spectrum resources and differences in link loads, network congestion is one of the key issues in cognitive radio wireless mesh networks. In this letter, a congestion avoidance model with power control, channel allocation, and routing under the signal‐to‐interference‐and‐noise ratio is presented. As a contribution, a nested optimization scheme combined with a genetic algorithm and linear programming solver is proposed. Extensive simulation results are presented to demonstrate the effectiveness of our algorithm.     

Maximum lifetime routing in wireless sensor networks

A routing problem in static wireless ad hoc networks is considered as it arises in a rapidly deployed, sensor based, monitoring system known as the wireless sensor network. Information obtained by the monitoring nodes needs to be routed to a set of designated gateway nodes. In these networks, every node is capable of sensing, data processing, and communication, and operates on its limited amount of battery energy consumed mostly in transmission and reception at its radio transceiver. If we assume that the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver then the energy consumption rate per unit information transmission depends on the choice of the next hop node, i.e., the routing decision. We formulate the routing problem as a linear programming problem, where the objective is to maximize the network lifetime, which is equivalent to the time until the network partition due to battery outage. Two different models are considered for the information-generation processes. One assumes constant rates and the other assumes an arbitrary process. A shortest cost path routing algorithm is proposed which uses link costs that reflect both the communication energy consumption rates and the residual energy levels at the two end nodes. The algorithm is amenable to distributed implementation. Simulation results with both information-generation process models show that the proposed algorithm can achieve network lifetime that is very close to the optimal network lifetime obtained by solving the linear programming problem.     

改进的遗传算法在多热源选址中的应用

多热源选址问题是一个多变量、多约束条件的线性规划问题。本文以遗传算法的基本思想为基础,对变异选择算子做了改进,并采取了字符编码技术而有效地压缩了约束条件,用二次选择的策略成功地加快了收敛进程,从而求得了多热源选址的全局最优解。