基于混合多种群自适应蚁群算法的无人机航路规划

针对基本蚁群算法在航路规划中易于过早陷入局部最优解,对蚁群算法进行了改进。提出了具有多种群的蚁群算法,并将导引因子引入到状态转移策略中,减少蚂蚁局部搜索的盲目性,确保蚂蚁最终完成航路搜索。当算法陷入局部收敛时,通过交换各种群的信息素,并对每个种群的挥发系数进行自适应调整,从而扩大了搜索空间,提高了搜索全局性。最后在代价函数简化后的栅格图中对改进算法进行了仿真。仿真结果表明,该方法可以有效防止算法陷入局部最优,是一种有效的航路规划方法。     

基于改进离散粒子群算法的测试优化选择

为了解决复杂系统测试性设计过程中测试选择所产生的组合爆炸问题,提出一种改进离散粒子群算法的智能方法。首先,为保证初始种群的多样性,利用混沌不重复遍历的特性初始化种群的速度和位置;其次,根据启发式规则和罚函数的方法计算粒子适应度,使算法具有良好的搜索性能;最后,通过采用自适应调整策略的惯性权重,使粒子易于跳出局部最优解,找到最优解。通过仿真实例验证了本文方法的有效性,优化结果满足系统各项测试性指标要求,可为复杂系统的测试优化选择提供有效指导。     

二冷配水优化建模与混合自适应PSO算法求解

针对连铸二冷区生产环境复杂且存在着大量水雾干扰的情况,建立了连铸水量优化模型并提出了一种混合的自适应粒子群算法来求解连铸二冷水优化问题。依据冶金过程中的工艺要求建立了二冷水量优化模型,并在经典的PSO算法基础上提出了适合该问题求解了混合自适应PSO算法。由于连铸过程存在着偏微分方程约束,传统的优化方法容易陷入局部最优解,不能达到很好的动态优化效果。研究了粒子群算法,基于种群的多样性,不断的自适应的更新粒子群算法中参数,将禁忌搜索的方法和传统的粒子群算法结合,增强了算法的局部搜索能力和全局寻找全局最优的能力。将该算法应用到连铸二冷水动态优化中,实验结果表面该算法能够快速有效的求解该优化问题。该方法用于连铸二冷水优化是可行的、有效的。     

角度受限下稀疏投影数据的改进粒子群优化随机CT重建

当计算机断层成像(CT)中X射线的采样范围和数量受限时,得到的稀疏投影数据完备性很低,重建算法的搜索空间巨大。基于凸优化思路的迭代求解算法及其改进采用固定搜索路径,难以在有限时间内收敛至全局最优解;粒子群优化具有全局搜索能力,但计算成本和存储代价过高。为解决这类不完备投影数据的重建问题,提出基于粒子群优化的随机稀疏重建算法。首先,通过随机策略生成具有多样性的初始种群,以保证算法的搜索能力;其次,随机选择梯度下降或基于个体历史最优解和全局历史最优解的随机方向进行迭代,以兼顾算法效率和搜索方向的多样性;最后,基于适应度评价,有针对性地重新生成随机初始种群,强制跳离局部最优。针对角度受限下无噪声和含噪声的稀疏投影数据,分别进行重建实验。结果显示,与常见的凸优化迭代和粒子群优化算法相比,本文算法既能保证算法效率,又在重建质量和算法稳健性上具有明显优势。     

基于A*算法的无人机跟踪目标的航迹规划

针对无人机跟踪目标的航迹规划问题,本文提出了一种双评估函数的改进A*算法。首先,根据无人机在跟踪目标时的飞行特点提出了航迹规划策略,并结合无人机的油耗、航迹长短和机动性能等约束条件来设计中间目标点的评估函数和航迹片段的评估函数。之后,采用加权法对A*算法进行改进,以使航迹的优化与时间耗费之间找到平衡点。同时,改进在Open表中插入与删除节点的方式,提高计算效率。最后,通过对跟踪航迹的仿真,表明该算法可以快速、有效地为无人机在跟踪目标时规划出优化的航迹。     

基于Voronoi图和动态自适应蚁群算法的UAV航迹规划

以UAV航迹规划为应用背景,提出了一种基于Voronoi图和动态自适应蚁群算法的航迹规划方法;为了提高航迹规划问题最优解的质量及全局求解能力,克服传统蚁群算法收敛速度慢、容易陷入局部最优等缺点,提出了一种动态自适应蚁群算法;采用动态自适应航迹点选择策略并将信息素更新规则和挥发系数进行动态自适应调整变化来对蚁群算法进行了改进,提高了算法的求解效率;根据战场已知威胁源生成Voronoi加权图,并与所提的动态自适应蚁群算法相结合求解规划空间中的最优航迹;考虑到UAV的物理约束限制,对生成的可行航迹进行平滑优化;仿真结果表明,该方法能够为UAV规划出一条满足要求的可飞航迹,验证了所提方法在解决航迹规划问题时是可行、有效的;     

蚁群元胞优化算法在人群疏散路径规划中的应用

针对疏散路径规划问题,以栅格化地图为背景的基础上,提出了蚁群元胞优化算法.首先为统一仿真时间步长,建立以六边形元胞为基础的栅格地图;然后利用静态势场对启发函数进行优化,利用分段更新规则优化信息素更新方式;最后,将模型参数作为粒子群优化算法的粒子位置信息进行优化,求解参数的最优组合值.仿真结果表明:采用蚁群元胞优化模型进行疏散路径规划时,不仅加快了搜索速度,而且增大了解空间,提高了搜索能力,可以有效避免陷入局部最优解.     

云环境下基于节能和负载均衡的混沌粒子群资源优化调度

针对传统云计算资源调度方法仅关注任务的最大完成时间,没有考虑到节能和资源负载均衡的问题,提出了一种基于混沌粒子群算法实现云资源优化调度的方法;首先,定义了以节能和负载均衡为目标的多目标数学模型,然后设计了一组靠近最优Pareto 前沿的解作为初始种群,采用改进的粒子群算法来搜索最优调度方案,当最优解连续两代未发生变化时,通过混沌遍历法对粒子进行局部寻优,以加快获取全局最优解;在CloudSim仿真环境下结合Matlab工具进行实验,结果表明:文中方法负载均衡离差平均值为0.156,且较其它方法,具有较好的负载均衡能力和较低的能耗,具有很强的可行性。     

利用三次样条改进蚁群算法的无人机航路规划

针对无人机在二维平面自动飞行中转弯角度过大、路径规划困难的问题,研究了蚁群算法在复杂环境下航路规划中的应用,利用链接图简洁的特点建立空间模型,对无人机的飞行环境和航迹代价进行了描述,并结合三次样条插值函数与蚁群算法,提出了改进蚁群算法,对无人机飞行路径进行优化,并给出算法软件流程。利用MATLAB进行了仿真实验,得出了最优的航路,算法具有较好的稳定性和鲁棒性,对轨迹中不可飞的尖角进行了平滑处理,使得航路为曲线轨迹,满足无人机工作的性能要求,减少无人机在飞行中的代价损耗,验证了该优化算法在无人机航路规划中的可行性。     

基于二进制粒子群算法的认知无线电决策引擎

提出了基于粒子群算法的认知无线电决策引擎,并提出了一种种群自适应粒子群算法,利用粒子群算法调整优化无线电参数,运用多载波系统对算法性能进行了仿真分析.实验结果表明基于二进制粒子群算法的认知决策引擎在收敛速度、收敛精度和算法稳定性上都要明显优于经典遗传算法,基于种群自适应粒子群算法的决策引擎则能进一步提高算法初期性能,满足认知无线电实时性要求. 关键词： 认知无线电 粒子群算法 遗传算法 认知决策引擎     

Region coverage-aware path planning for unmanned aerial vehicles: A systematic review

The use of unmanned aerial vehicles (UAVs) to carry out remote aerial surveys has become prominent in recent years. The UAV-based survey faces several operational issues, such as complicated terrain, limited UAV resources, obstacles, sensor limitations, and other environmental factors. In addition, the coverage plan includes numerous objectives, such as lowering path length, maximizing coverage, and limiting survey time, necessitating multi-objective optimization. UAVs require effective coverage path planning (CPP) to generate the ideal route. It involves determining the path which encompasses every point inside the required region under different constraints. The process automates the process of route determination for autonomous operation by considering various environmental constraints. In this paper, we explore and analyze the existing research on the various techniques used in coverage route planning for UAVs. It provides an overview of the current state-of-the-art CPP methods for UAVs. The study discusses the key challenges and requirements of CPP for UAVs and presents various approaches proposed in the literature to tackle these challenges. We explore a variety of geometric flight patterns for the area of interest having UAV deployment. It also features multi-UAV and multi-region coverage strategies, providing a new dimension to UAV-based operations. The energy consumption of UAVs during CPP is an essential factor, as it influences their flight length and mission duration. The design of the CPP algorithm is determined by the unique requirements of the UAV application, such as the size and form of the region to be mapped, the existence of obstacles, and the desired coverage resolution. Path planning strategies in a three-dimensional environment and dynamic coverage are also included in the study. Moreover, we compare the existing strategies using different performance metrics to evaluate the success of covering missions. Finally, the problems and unresolved concerns related to UAV coverage path planning are examined to provide valuable insights to the readers.     

无人机多阶段航迹预测协同任务规划

针对多无人机(Unmanned Aerial Vehicles, UAVs)协同控制问题,提出了一种UAVs多阶段航迹预测分布式任务规划方法。定义从一次任务分配开始到其中一项任务完成为一个任务周期。在每个规划周期,首先,各UAV使用A*算法快速预测到所有任务目标的路径,提供至任务分配;然后,采用聚类算法修改目标价值向量,协商分配结果,并实时计算探测范围内的最短路径;最后,采用三次B样条曲线平滑所分配的最短路径,在线规划出满足飞行约束的飞行航迹。通过仿真实验对算法的有效性进行了验证,结果表明,提出的算法能够实时获得近似最优的任务分配结果并规划出可飞行航迹,并有效处理突发任务。     

Multi-objective optimization of inverse planning for accurate radiotherapy

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly,the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ)was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dosevolume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set.     

基于PSO算法的RoboCup2D机器人研究

对机器人体系结构、动作学习及行为的组织方式进行了研究,以演化计算为基本方法,以RoboCup2D为平台,设计了基于PSO算法的足球机器人的体系结构,解决感知、动作、和规划问题;在训练环境下,形成感知规则,优化感知相关参数,得到对信息高效快速的感知方法,并根据指定的粒度、功能、参数,对RoboCup2D机器人的原子动作进行了组合优化,得到一组带参数和执行效果描述的粒子动作;最后在赛场环境和任务驱动下,搜索粒子动作并进行组织规划,得到完成特定任务的机器人行为;RoboCup2D仿真实验表明,演化计算方法不仅能利用原子动作进行组合优化,得到适应于不同条件的粒子动作,而且能通过其在线搜索粒子动作,动态组成机器人行为;基于演化计算的足球机器人能更好地完成跑位、截球、带球、传球等任务,具有更强的适应性。     

Energy spectra considerations for synchrotron radiotherapy trials on the ID17 bio‐medical beamline at the European Synchrotron Radiation Facility

The aim of this study was to validate the kilovoltage X‐ray energy spectrum on the ID17 beamline at the European Synchrotron Radiation Facility (ESRF). The purpose of such validation was to provide an accurate energy spectrum as the input to a computerized treatment planning system, which will be used in synchrotron microbeam radiotherapy trials at the ESRF. Calculated and measured energy spectra on ID17 have been reported previously but recent additions and safety modifications to the beamline for veterinary trials warranted a fresh investigation. The authors used an established methodology to compare X‐ray attenuation measurements in copper sheets (referred to as half value layer measurements in the radiotherapy field) with the predictions of a theoretical model. A cylindrical ionization chamber in air was used to record the relative attenuation of the X‐ray beam intensity by increasing thicknesses of high‐purity copper sheets. The authors measured the half value layers in copper for two beamline configurations, which corresponded to differing spectral conditions. The authors obtained good agreement between the measured and predicted half value layers for the two beamline configurations. The measured first half value layer was 1.754 ± 0.035 mm Cu and 1.962 ± 0.039 mm Cu for the two spectral conditions, compared with theoretical predictions of 1.763 ± 0.039 mm Cu and 1.984 ± 0.044 mm Cu, respectively. The calculated mean energies for the two conditions were 105 keV and 110 keV and there was not a substantial difference in the calculated percentage depth dose curves in water between the different spectral conditions. The authors observed a difference between their calculated energy spectra and the spectra previously reported by other authors, particularly at energies greater than 100 keV. The validation of the beam spectrum by the copper half value layer measurements means the authors can provide an accurate spectrum as an input to a treatment planning system for the forthcoming veterinary trials of microbeam radiotherapy to spontaneous tumours in cats and dogs.     

Evaluation of the applicability of MOSFET detectors in radiotherapy

MOSFET detectors of 1 mVcGy⁻¹ sensitivity were tested for the accuracy of absorbed dose measurements in radiation therapy with the use of photon and electron beams. Before a detector was used in the study, several calibration coefficients were determined to allow for different factors affecting its operation. Then, the detector exposure response was compared with the dose calculated in the anthropomorphic phantom by the Monaco and MasterPlan treatment planning systems. MOSFET detectors were placed inside the phantom during the irradiation. Three different plans for thorax and pelvis areas were studied. The paper presents the differences between planned and MOSFET measured doses delivered to the selected target areas using conventional and IMRT techniques.     

Multi-objective optimization of inverse planning for accurate radiotherapy

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set.     

Metropolitan Optical Networks: When to Change to DWDM

In this article we propose a new methodology to estimate the change in the bandwidth demand in time, mainly in residential areas. The bandwidth demand forecast is a basic aspect to decide the evolution of metropolitan optical networks. The word evolution refers to the necessity of making changes in networks, either by upgrading or replacing different elements that are part of the network. This analysis, along with a technological alternatives analysis, provides the operator the needed information to achieve the appropriate network planning and to make investment decisions.     

基于RDC-PSOA的复杂自动化控制最优任务规划方法研究

在复杂的自动化控制模型中进行最优任务规划数据推荐时,容易出现只将任务分配给最先执行的处理器的情况,导致个别处理器上任务分配过多,造成整体时间跨度增加,提出一种基于推荐数据特点粒子群优化(Recommended data characteristics-Particle swarm optimization algorithm: RDC-PSOA)的复杂自动化控制最优任务规划方法,以一群随机粒子为初始解,依据复杂自动化控制模型中任务规划数据推荐问题的特点,在粒子群算法的基础上,重新塑造粒子描述形式,对粒子的位置与速度进行编码,将粒子群算法映射到离散空间,通过迭代获取全部可能的自动化控制任务规划方案,实现数据的有效推荐。仿真实验结果表明,所提方法不仅具有很强的收敛能力,而且数据推荐完成时间短,性能优越。     

Metropolitan Optical Networks: When to Change to DWDM

In this article we propose a new methodology to estimate the change in the bandwidth demand in time, mainly in residential areas. The bandwidth demand forecast is a basic aspect to decide the evolution of metropolitan optical networks. The word evolution refers to the necessity of making changes in networks, either by upgrading or replacing different elements that are part of the network. This analysis, along with a technological alternatives analysis, provides the operator the needed information to achieve the appropriate network planning and to make investment decisions.