Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Satellite Constellation Design with Adaptively Continuous Ant System Algorithm

The ant system algorithm （ASA） has proved to be a novel meta-heuristic algorithm to solve many multivariable problems. In this paper, the earth coverage of satellite constellation is analyzed and a n ＋ 1^ -fold coverage rate is put forward to evaluate the coverage performance of a satellite constellation. An optimization model of constellation parameters is established on the basis of the coverage performance. As a newly developed method, ASA can be applied to optimize the constellation parameters. In order to improve the ASA, a rule for adaptive number of ants is proposed, by which the search range is obviously enlarged and the convergence speed increased. Simulation results have shown that the ASA is more quick and efficient than other methodV211.71s.

Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization

A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount operation.It is the most complex and time-consuming stage in the production process.Therefore,it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line.In this study,according to the specific type of chip mounter in the actual production line of a company,a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line.The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter.On this basis,a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter.The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm.It combines the advantages of the two algorithms and improves their global search ability and convergence speed.The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.

一种多策略改进鲸鱼优化算法的混沌系统参数辨识

针对混沌系统参数辨识精度不高的问题,以鲸鱼优化算法(whale optimization algorithm,WOA)为基础,提出一种多策略改进鲸鱼优化算法(multi-strategy improved whale optimization algorithm,MIWOA)。采用Chebyshev混沌映射选取高质量初始种群,采用非线性收敛因子和自适应权重,提高算法收敛速度,为了避免算法陷入局部最优,动态选择自适应t分布或蚁狮优化算法更新后期位置,提高处理局部极值的能力。通过对10个基准函数和高维测试函数进行仿真试验,表明MIWOA具有良好的稳定性和收敛精度。将MIWOA应用于辨识Rossler和Lu混沌系统参数,仿真结果优于现有成果,表明本文MIWOA辨识混沌系统参数的高效性和实用性。

基于模糊控制蚁群算法的移动机器人路径规划

AGV(automated guided vehicle)等移动机器人在不同环境,特别是复杂环境中的路径规划存在着算法收敛速度慢、收敛路径冗余节点多的问题。为克服上述问题,提出一种基于改进蚁群算法的全局路径规划算法(FOACO)。首先,引入一种新的信息素初始化分布规则,提高初始信息素的启发作用;同时,在传统蚁群算法的信息素更新策略中引入模糊控制器和收敛状态信息素增量参数,来提高信息素更新效果和启发作用;引入新的自适应调整信息素权重因子和蒸发率规则,进一步提高算法的收敛速度和最优路径搜索能力;引入几何优化算法进行冗余节点的去除和最短路径的更新优化,动态更新最终路径。实验结果表明,在不同仿真环境,FOACO算法可以有效地减少最优路径搜索的迭代次数,加快算法收敛速度,进一步优化路径冗余节点、缩短最优路径,整体效果优于传统蚁群算法。

基于多目标PSO-ACO融合算法的无人艇路径规划

【目的】针对河蟹养殖过程中,水位变化以及无人艇路径规划算法收敛慢、精度低的问题,为提高算法适应性与寻优能力,提出一种多目标粒子群−蚁群融合的无人艇路径规划算法。【方法】首先,分析蟹塘环境及养殖规律等因素,建立静态水深栅格环境模型;其次,针对覆盖遍历式投饵存在局部点投喂不足及路径次优的问题,通过对惯性参数与学习因子的非线性调整,提出基于多目标的改进粒子群算法(Particle swarm optimization,PSO);然后,调整蚁群算法的初始信息素,并对蚁群算法的信息素挥发因子和启发期望函数自适应改进,提出自适应优化蚁群算法(Ant colony optimization,ACO);最后,为解决单一算法寻优不足,利用融合PSO-ACO算法,实现无人艇多目标全局路径规划。【结果】仿真结果表明:不同环境投饵策略下,PSO-ACO算法在对多目标路径寻优时,不仅环境适应性好,而且提高了寻优效率和精度,运行时间节省了32%,路径距离缩短了9.78%,迭代次数降低了62.88%,拐点数目减少了44.45%。【结论】所提出多目标点的路径规划算法适用于环境可变的蟹塘养殖,具有较好的应用价值。

Ant Lion Algorithm for Optimized Controller Gains for Power Quality Enrichment of Off-grid Wind Power Harnessing Units

The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.

改进并行蚁群算法在配电网网架优化中的应用

针对蚁群算法收敛速度慢、易陷入局部极小的不足,在并行蚁群算法的基础上,改进组间蚂蚁沟通交流方式,并提出一种自适应分组策略,在算法运行过程中,每过一定迭代次数,将蚂蚁的组数减半,每组蚂蚁的数量倍增,直至为一组蚂蚁。每次蚂蚁组数减半时,采用一种组间信息素融合规则更新留存组蚂蚁路径信息素。通过一个配电网网架优化问题的实例进行实验,仿真结果表明改进算法在收敛速度和寻优方面都有所提升。

基于Voronoi空间单元的林分空间结构智能优化研究

使用Voronoi法确定对象木与相邻木存在边缘校正不准确和阈值大小不确定等问题,不利于林分空间结构量化描述。针对这些问题,本研究设定所有距离样地边界线小于0.5 m且不完整的Voronoi单元中的林木为非对象木,并引进对象木与相邻木的距离调控阈值Rmax,修正了Voronoi空间单元在边缘效应处理上存在的对象木和相邻木选取不合理问题。基于修正的Voronoi空间单元,构建了多目标生态采伐方案,并通过蚁群算法进行智能优化。结果表明,调控前20个样地中4个林分空间结构状况相对较差的样地,通过模拟采伐调控后,适应度函数分别提升0.72,0.92,0.93,0.86,证明了该方案对于优化林分空间结构具有较好的效果,可为森林经营决策提供支持工具。

自适应增强优化的瓦斯涌出量预测模型

为提升回采工作面瓦斯涌出量的预测能力,提出一种基于门控循环单元(gate recurrent unit,GRU)预测模型,利用瓦斯涌出相关影响因素对瓦斯涌出量进行预测。对麻雀搜索算法的初始化过程加以改进,采用改进后的麻雀算法对影响GRU预测模型的超参数进行优化,提高瓦斯涌出量的预测精度;利用AdaBoost算法的自适应增强能力,构建自适应增强优化的瓦斯涌出量预测模型(ISSA-GRU-AdaBoost模型),并通过核主成分分析提取预测指标特征,提升预测的快速性。将所建模型与PSO-ELM模型、QPSO-LSTM模型、PSO-BP模型,以及SSA-SVM模型进行对比实验,结果表明ISSA-GRU-AdaBoost预测模型的预测精度最高。

基于Q学习的蚁群优化水声网络协议

针对水声通信中数据传输延时高且动态适应性弱的问题,提出了一种基于Q学习优化的蚁群智能水声网络路由协议(Q-learning ant colony optimization, QACO).协议包括路由行为和智能决策部分,在路发现和维护阶段,依靠网络智能蚂蚁进行网络拓扑环境的构建和节点之间的信息交换以及网络的维护.在Q学习阶段,通过定量化节点能量和深度以及网络传输延时学习特征作为折扣因子和学习率,以延长网络的生命周期,降低系统能耗和延时.最后通过水声网络环境进行仿真,实验结果表明QACO在能耗、延迟和网络生命周期方面都优于基于Q学习辅助的蚁群算法(Q-learning aided ant colony routing protocol, QLACO)和基于Q-learning的节能和生命周期感知路由协议(Q-learning-based energy-efficient and lifetime-aware routing protocol, QELAR)和基于深度路由协议(depthbased routing, DBR)算法.

基于改进蚁群算法的飞机低空突防航路规划(英文)

The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained.

一种动态自适应蚁群算法

针对传统蚁群算法容易出现早熟和停滞现象的缺陷,提出了一种动态自适应蚁群算法。该算法对传统的MMAS蚁群算法中的信息素进行自适应调整。实验结果表明,该算法比传统的蚁群算法和传统的MMAS蚁群算法具有更好的搜索全局最优解的能力,并具有更好的稳定性和收敛性。

多阶段输电网络最优规划的并行蚁群算法

多阶段输电网络最优规划是一个复杂的非线性组合优化问题,难以采用传统的数学优化方法求解。蚁群算法是近年来出现的用于解决组合优化问题的一种高效的内启发式搜索技术,但存在着未成熟收敛问题。文中给出了多阶段输电网络最优规划的数学模型及其解的向量形式;详细分析了传统蚁群算法的未成熟收敛现象及其原因;提出一种并行蚁群算法并用于求解多阶段输电网络最优规划问题。并行蚁群算法无需初始可行解,能很好地协调局部搜索与全局搜索,在加快计算速度的同时有效地避免了因参数设置、种群规模等不同而引起的未成熟收敛。对实际算例的计算结果表明,该方法具有很高的计算效率和良好的全局收敛性。

基于蚁群优化算法的机组最优投入

机组最优投入问题(optimal Unit Commitment,UC)是寻求1个周期内各个负荷水平下机组的最优组合方式及开停机计划,使运行费用为最小。该问题是一个高维数、非凸的、离散的、非线性的优化问题,很难找出理论上的最优解,但由于它能带来显著的经济效益,所以受到了国内外很多学者的广泛关注。作者尝试采用一种新型的模拟进化优化算法——蚁群优化算法(ACO)来求解该问题。首先,利用状态、决策及作者提出的路径概念把UC设计成类似于旅行商(TSP)问题的模式,从而可以方便地利用ACO来求解。其次,由于ACO处理的是无约束优化问题,对于UC这一约束优化问题,提出了不同的方法来处理各种约束。用tabu表限制不满足旋转备用约束和机组最小启/停时间约束的状态;通过附加惩罚项来处理线路N安全性约束。数值算例验证了此算法的可行性和有效性。

用于机组组合优化的蚁群粒子群混合算法

提出了一种用于求解机组组合优化问题的蚁群粒子群混合优化算法。通过将机组组合解编码为机组操作序列,降低了蚁群算法搜索的难度,使其空间复杂度由指数型降为线性型,使采用蚁群算法求解更大规模的机组组合问题成为可能。采用协同粒子群算法求解多时段负荷的经济分配问题时,用一个粒子群处理一个时段的优化问题,通过共享粒子群间的惩罚项解决了机组爬升率的约束问题。10机和20机系统的仿真实验和分析结果验证了该方法正确性、有效性和优越性。

基于自适应蚁群算法的协同制造项目资源优化配置

为探索更高效的协同制造资源优化配置算法,提出了基于自适应蚁群算法的求解方法。针对零件的特定工艺线路,建立了以时间、成本和质量为目标的制造资源优化配置模型,设计了模型求解的自适应蚁群算法,并给出具体算例,验证了算法的可行性和有效性。

基于蚁群系统的参数自适应粒子群算法及其应用

为了解决粒子群算法惯性权重自适应问题,提出一种基于蚁群系统的惯性权重自适应粒子群算法(AS-PSO).AS-PSO首先将惯性权重取值区间离散化,各个惯性权重子区间在初期赋予相同的信息素;然后,粒子群算法中的各个粒子,根据各个惯性权重子区间中的信息素浓度和粒子在搜索空间中分布的先验知识,确定各个惯性权重子区间的选择概率,并进而实现粒子的空间搜索;最后,基于粒子的进化信息,实现惯性权重子区间信息素浓度的更新.仿真研究表明,AS-PSO算法在种群进化寻优的同时,能根据种群的进化信息,通过蚁群算法实现惯性权重参数的自适应调整和进化,且不增加测试函数的调用次数;算法寻优性能优于传统的自适应粒子群算法和根据速度信息自适应调整参数的粒子群算法.同时,算法实际应用于复杂系统模型参数的优化估计,获得满意结果.

基于改进自适应遗传算法的电力系统相量测量装置安装地点选择优化

将进化参数衰减因子与基于适应度变化的自适应遗传算法相结合,提出了一种新的自适应遗传算法,使遗传算法在进化过程中能够同时根据个体适应度和进化时间的变化自动调整交叉与变异概率,克服了原有自适应遗传算法易早熟的缺点,提高了最优解的多样性和寻优速度。精英个体保留策略保证了整个算法的全局收敛性。在约束条件处理时,采用了不可行解启发性修复方法,提高了算法的优化效果。基于图论的深度优先方法用于系统可观性分析。将新的自适应遗传算法应用于优化相量测量装置安装地点选择,实现了安装地点最少,而整个系统可观的目标。该算法已在某省46节点系统的优化计算中得到了验证。

导弹并行测试系统设计与任务调度仿真

针对传统串行测试时间长、效率低、成本高等问题,利用并行性程序设计、仿真了导弹测试系统,同时基于蚁群算法提出了并行测试系统任务调度算法.并行测试系统的硬、软件包括可重构测试接口适配器、同步类以及仪器类组合方法等.算法中设计了局部搜索函数、状态转移概率、基于Tchebycheff不等式的信息素更新和测试禁忌计算式,配置了测试任务优先级,从而获得了测试任务最优调度方案.仿真结果表明,所提算法的测试时间是串行测试的30%～75%,平均搜索时间和找到最优解的概率等较之TaskScheduler-T调度和遗传退火算法优越性更加显著.