Neural network and genetic algorithm based global path planning in a static environment

Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.

Optimization of UMTS Network Planning Using Genetic Algorithms

The continuously growing of cellular networks complexity, which followed the introduction of UMTS technology, has reduced the usefulness of traditional design tools, making them quite unworthy. The purpose of this paper is to illustrate a design tool for UMTS optimized net planning based on genetic algorithms. In particular, some utilities for 3G net designers, useful to respect important aspects (such as the environmental one) of the cellular network, are shown.

A Genetic Algorithm for Overall Designing and Planning of a Long Term Evolution Advanced Network

In the mobile radio industry, planning is a fundamental step for the deployment and commissioning of a Telecom network. The proposed models are based on the technology and the focussed architecture. In this context, we introduce a comprehensive single-lens model for a fourth generation mobile network, Long Term Evolution Advanced Network (4G/LTE-A) technology which includes three sub assignments: cells in the core network. In the resolution, we propose an adaptation of the Genetic Evolutionary Algorithm for a global resolution. This is a combinatorial optimization problem that is considered as difficult. The use of this adaptive method does not necessarily lead to optimal solutions with the aim of reducing the convergence time towards a feasible solution.

Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm

Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.

Application of Interval Algorithm in Rural Power Network Planning

Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.

Multi-objective Transmission Expansion Planning Considering Life Cycle Cost

为克服目前全寿命周期成本（life cycle cost,LCC）技术的应用局限于设备运行或维护阶段的不足,针对输电网整体建立一个3维LCC层级模型,包括时间维度、元件维度和费用维度。费用维度进一步分解为设备级、系统级、外部环境成本。研究了以可靠性为中心的维护手段的应用对维护成本的影响,利用电量不足期望值计算故障成本。在此基础上,对风电等4种不确定因素进行建模,建立以LCC成本最小和切负荷量最小为多目标的输电网机会约束规划模型。采用正态边界交点算法联合改进小生境遗传算法,对模型有效求解。最后,分别对18节点系统和77节点系统进行算例分析。研究结果给出了帕累托解集及推荐的最优规划方案;此外,LCC费用分解图表明了各费用的比重和影响,有利于指导未来资产管理。

Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

Fuzzy-GA based algorithm for optimal placement and sizing of distribution static compensator (DSTATCOM) for loss reduction of distribution network considering reconfiguration

This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.

Optimal transmission lines assignment with maximal reliabilities in multi-source multi-sink multi-state computer network

The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.

A New Genetic Algorithm Applied to Multi-Objectives Optimal of Upgrading Infrastructure in NGWN

A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem.

具有FACTS设备的输电网络规划

利用改进的遗传算法ＭＡＧＡ进行具有ＦＡＣＴＳ设备的输电网络规划，算例系统的结果表明ＭＡＧＡ适用于解决具有ＦＡＣＴＳ设备的电网规划问题，而且在电网的扩建和改造方面，ＦＡＣＴＳ技术将带来很大的经济效益。

基于智能计算的多FACTS协调配置

灵活交流输电(flexibleAC transmission system,FACTS)装置在电力系统中应用广泛,但通常各台FACTS设备都是针对本地量和各自目标进行参数整定。为了更大程度地发挥FACTS效用,消除潜在的不利交互影响,有必要对多FACTS进行参数的协调配置。文中首先采用小波变换分析了协调配置的必要性,结合改进的多目标量子遗传算法和极限学习机提出了多FACTS协调配置算法,最后在装设有TCSC和SVC的算例中进行仿真,验证了所提算法的有效性。

基于近似模型的弹性背腔穿孔管消声器声学性能优化

弹性背腔穿孔管消声器利用声波和弹性管壁的耦合作用,相比于穿孔管消声器具有更好的低频消声性能。为了进一步提高该消声器的低频、宽频消声能力,采用遗传算法对其进行优化。首先,利用COMSOL有限元软件建立该消声器的传递损失计算模型。其次,分析弹性管壁材料对消声器低频消声性能的影响,并选取橡胶复合材料作为弹性管壁材料。再次,由于所采用的有限元模型计算耗时长,为节约时间资源,利用径向基函数(Radial Basis Function,RBF)神经网络方法建立近似模型代替有限元计算模型。最后,利用近似模型结合遗传算法进行全局寻优,获得该消声器结构参数的优化结果,并利用有限元模型验证优化的准确性。结果表明:优化后在100~1000 Hz低频段范围内弹性背腔穿孔管消声器消声带宽增加62%,且平均传递损失提高3.6 dB。

改进RBF神经网络在智能机器人轨迹规划中的研究

针对工业生产中对智能机器人轨迹规划的要求越来越高,在工业机器人运动模型的基础上,提出了一种将RBF神经网络和遗传算法相结合的工业机器人轨迹规划方法。通过遗传算法对RBF神经网络的网络结构、连接权值和阈值进行优化,精确跟踪机器人的轨迹。通过仿真将与未改进前的轨迹规划算法进行比较,验证该方法的优越性。结果表明,与改进前的规划算法相比,文中规划方法误差小,适应性强,能够满足工业机器人轨迹规划的预期要求。为工业机器人轨迹规划方法的发展提供了一定的参考。

含大规模分布式光伏接入电网的无功电压控制降损方法研究

大规模分布式光伏(distributed photovoltaic,DP)接入电网后,电网电压易出现大幅度波动,导致电网损失问题越来越突出。当前已有的电网降损方法没有考虑光伏发电自身的无功调节能力,应用效果不理想。为此,设计一种含大规模DP接入电网的无功电压控制降损方法。通过建立目标函数,设定约束条件,采用敏感性分析方法分析电压稳定性,将敏感度较高的值作为无功补偿位置,并对当前负荷进行预测,确定最佳的补偿容量,实现电网降损。试验结果表明:所提方法降损后较降损前基准电压更趋于1 pu,更为稳定,所提方法能够针对光伏控制接入分布特性,有效控制无功功率变化情况,相对于其他方法,无功功率波动较小,表现出较好的降损效果。

具有FACTS设备的多目标电网规划

提出了具有灵活交流输电 (FACTS)设备的多目标妥协规划模型 ,综合考虑了FACTS设备对电网规划的经济性和可靠性两方面的影响 ,并采用混合遗传—模拟退火算法求解了该模型。算例结果表明FACTS设备能够提高电网规划的经济性及经济性与可靠性的综合水平 。

中欧集装箱多式联运服务网络设计

中欧运输通道和运输方式不断发展完善,海运、中欧班列及多种运输方式联运等构成了中欧间集装箱运输服务网络。货主在选择运输服务时,一直关注货物运输的费用与时效性,由于中欧间运输距离长,节点多,节点作业时长还存在很大的不确定性。同时,随着全球对碳排放问题的重视,运输服务产生的碳排放也成为货主考虑的因素。综合考虑运输费用、时间和碳排放的影响,并关注节点作业时间的不确定性,解决中欧集装箱多式联运服务网络设计问题具有非常重要的现实意义。建立最小化运输费用、运输时间和运输碳排放量的多目标多式联运服务网络设计模型,并在模型中引入不确定性时间变量。由于节点作业时间样本数据有限,通过Box-Muller变换生成随机数丰富数据,并减少不可观测的误差,运用蒙特卡洛模拟对运输时间进行不确定性统计,描述总运输时间的统计特征。基于多目标的Pareto最优思想,设计了快速非支配排序遗传算法求解最优运输服务方案。以天津至汉堡的中欧集装箱运输为实例,根据实际调研结果确定各项相关参数设定,进行模型和算法验证,求解得到多式联运运输方案的Pareto最优解集。结果显示不同的运输服务方案其运输费用、运输时间、碳排放量各有差异,并且符合Pareto最优解集定义,证明了研究提出的考虑不确定性的多目标服务网络设计建模及算法的正确性和可行性,研究成果可为货主提供选择符合其需求的不同运输服务优化方案。

高速铁路网列车运行图接续方案优化方法

我国高速铁路网中多列车之间在多车站进行接续时的关系灵活且复杂,为有效提升旅客中转服务质量,先构建列车运行图接续方案优化的整数规划模型,调整既有运行图中列车的停站方案和运行时刻,优化列车接续的时间、频率和方式;再根据问题特点,在线性化模型的基础上设计遗传算法,将列车停站和运行时刻的循环迭代决策纳入遗传算法框架,提高模型求解效率;最后依托“二横二纵”主干路网构建大规模计算案例,验证方法的有效性。结果表明:与实际列车接续方案相比,优化后列车之间的接续时间平均缩短11 min,接续频率总计提高1109次,共53组列车接续由异站改善为同站,74.94%的客流OD中转服务质量得到不同程度提升;客流OD中的换乘需求越多,中转服务质量提升幅度越大,列车接续服务资源对不同OD间客流需求的匹配度更好。该方法能够在较小幅度调整既有运行图的前提下,获得列车接续方案较大幅度的质量提升。

基于启发式遗传算法的通信光缆网络线路规划布局方法

由于通信光缆网络线路规划受到多种限制条件的制约,导致最优解比例低。针对上述问题,提出基于启发式遗传算法的通信光缆网络线路规划布局方法。通过建立数学模型明确目标和约束条件,利用启发式遗传算法进行线路初始化,并通过选择、交叉、变异等方法持续优化群体,直至满足终止条件。实验结果表明:这种方法通过明确约束条件,获取高比例最优解,为通信光缆网络线路规划布局提供了更优方案。

基于改进遗传退火算法的配电网路线协调规划方法

为提高电力系统的整体效率及供电的可靠性和稳定性,满足不同地区和不同用户的电力需求,提出了基于改进遗传退火算法的配电网路线协调规划方法。首先,建立配电网路线布局模型,获取可用裕度较大的配电网路线布局方案;其次,基于时间序列,计算预测对象历史年时间序列数据的平均增长率,得出预测对象各年的负荷预测值;在此基础上,利用改进遗传退火算法,寻找最优解,实行配电网路线协调规划。实验结果表明,应用该方法后,配电网路线的覆盖率得到了显著提升,整体规划效果更好,能满足更多用户的需求,实现电力资源的合理分配和利用。