通行受限下需求可拆分的应急物资卡车-多无人机协同配送路径优化

研究通行受限情景下需求可拆分的应急物资卡车-多无人机协同配送路径优化问题,综合考虑灾区路网状况、卡车可途中发射/接受无人机、无人机单次起飞可配送多个需求点、需求可拆分等因素,以应急物资配送任务完成时间最短为目标,构建卡车-多无人机协同配送路径优化模型.根据问题与模型特征设计一种改进蚁群算法求解.实验结果表明:文章方法能合理分配卡车与无人机的配送任务,科学规划通行受限情景下需求可拆分的应急物资卡车-多无人机协同配送路径;卡车途中发射/接收无人机方式能有效缩短无人机飞行距离,减少卡车与无人机的协同时间,缩短通行受限情景下的应急物资配送时间,具有可行性、合理性与有效性.     

基于拟态物理学的约束多目标共轭梯度混合算法

在拟态物理学优化算法APO的基础上,将一种基于序值的无约束多目标算法RMOAPO的思想引入到约束多目标优化领域中.提出一种基于拟态物理学的约束多目标共轭梯度混合算法CGRMOAPA.算法采取外点罚函数法作为约束问题处理技术,并借鉴聚集函数法的思想,将约束多目标优化问题转化为单目标无约束优化问题,最终利用共轭梯度法进行求解.通过与CRMOAPO、MOGA、NSGA-II的实验对比,表明了算法CGRMOAPA具有较好的分布性能,也为约束多目标优化问题的求解提供了一种新的思路.     

多无人机对组网雷达的协同干扰问题研究

在现代战场中,航迹欺骗干扰技术是针对组网雷达提出的协同干扰技术,旨在利用多架无人机在同一时刻对组网雷达中的部分雷达实施相互关联的虚假目标欺骗干扰.充分考虑了实际情况下无人机飞行模态、飞行速度、飞行高度的约束限制,对雷达的位置分布以及虚假航迹线进行可视化,并将三维空间模型投影到二维平面后进行建模分析.主要针对一个虚拟的无人机航迹欺骗干扰问题,建立时空模型,通过构建可达交互矩阵和雷达扫描算法,结合0-1整数规划,得到无人机协同飞行的最优策略.     

多无人机协同任务规划问题

研究无人机任务规划问题,从无人机侦查和轰炸两方面入手.首先,运用迭代算法求解出从基地到雷达区域边际上任一目标出入口的最短路径.在此基础上,以无人机在雷达范围内滞留时间最短,以及被探测次数最少为目标,建立多目标最优化模型.通过改进交叉算子的遗传算法找出最优侦查路径.对于轰炸任务,以无人机在雷达范围内滞留时间最短,以及轰炸总时间最短为目标,建立多目标最优化模型.采用改变惯性权重的自适应粒子群算法找出最佳轰炸路线.由于计算时间较长,本文对68个目标进行聚类分析,提出针对轰炸任务的快速算法,相较原轰炸方案,其计算效率提高80%以上.     

复杂约束条件下的UCAV多重任务分配

与单任务分配问题相比,无人作战飞机(UCAV)多重任务分配具有更复杂的约束条件.基于UCAV任务分配有向图给出了多重任务分配的一般框架,分析了任务分配的约束条件,提出了一种求解约束优化问题的改进遗传算法.其基本思想是对种群中的个体按两种方案评价,对可行解按目标函数值大小,对不可行解按约束违反程度进行评价,避免了罚函数法中的罚因子的选取难题.采用矩阵形式进行个体编码,按目标出现顺序映射任务类型,解决了多重任务的编码表示,并对武器类型向量实施绑定策略,简化了问题复杂性.设计了选择,交叉,变异等遗传操作算子,保证生成的新染色体不破坏编码时满足的约束条件.最后进行了仿真试验,结果表明提出的方法求解UCAV多重任务分配问题的可行性和有效性.     

复杂地势的无人机灾情巡查新方法

主要针对无人机在抢险救灾中的灾情巡查问题探究新型巡查方法.通过变"覆盖巡查面"为"有效巡查点",将传统的多无人机协同覆盖巡查问题转化为带有避障的VRP问题,并以所有无人机总飞行时间最少为目标建立相应的数学模型.运用MATLAB编写蚁群算法求解VRP中任意两点间的最短避障路线长度矩阵,进而用遗传算法来求解带有避障的VRP问题,得到不同需求下的最少无人机数量,规划出飞行路线.经过案例分析可得,此巡查路线覆盖率达到85.95%,具有较好的实用性.     

一种无人机侦察航迹分层规划方法

给出了一个通用可行的无人机侦察航迹分层规划方法,并应用到第十三届"华为杯"全国研究生数学建模竞赛A题第一问中.将无人机侦察航迹规划问题划分为四个层次,从上至下分别是目标群间侦察顺序优化,目标群内各目标侦察顺序优化,侦察点位优化,转弯设计,依次求解获得侦察航迹.通过分层解算方法既有效控制了算法复杂度,又能在确保满足复杂约束的同时优化无人机在敌方雷达探测区域内的暴露时间.     

无人机在次生灾害巡查中的优化运用

运用优化工具与优化方法对无人机在抢险救灾过程的次生灾害巡查阶段进行了系统性的研究.首先确立巡查的区域范围,采用往返式"Z"型航行路线,结合无人机的续航时间确定无人机的全部行走路线.根据每架无人机在有效时间内可航行的总路径,结合优化计算将整个巡查区域划分为多个小区域.基于改良的贪婪算法求解出各个小区域的最佳无人机探测方案,以期获得整体最优飞行路线.由于每个小区域在一定时间间隔内需要重复巡查,对每个小区域进行细致的路径规划和无人机数量安排,确保满足巡查要求.     

多无人机对组网雷达的协同干扰

针对无人机编队与组网雷达之间的欺骗干扰问题,结合无人机编队与组网雷达的工作特性,首先采用分层规划的思想建立了无人机编队的航迹搜寻、协同规划、安全约束模型,根据0-1规划的方法制定出匀速直线等约束下无人机编队的协同策略,其次利用改进的航迹搜寻模型进行虚假航迹搜寻,使固定数量的无人机编队新增出4条虚假航迹.     

基于精英蚁群算法的多目标生鲜配送路径优化研究

随着人们对于食品质量要求的提高,近年来绿色生鲜配送受到了社会的普遍关注.首先对配送过程中客户满意度、总成本、大气污染物和温室气体排放量三个目标进行了分析,接着建立了多目标配送路径优化模型,最后以最大化客户满意度作为主要目标利用精英蚁群算法进行求解,为生鲜配送基地提出的三个配送优化方案设计了最优配送路径.配送基地可根据自身发展选择合适的配送方案.结果也验证了模型的正确性和算法的可行性.     

Assigning cooperating UAVs to simultaneous tasks on consecutive targets using genetic algorithms

A problem of assigning multiple agents to simultaneously perform cooperative tasks on consecutive targets is posed as a new combinatorial optimization problem. The investigated scenario consists of multiple ground moving targets prosecuted by a team of unmanned aerial vehicles (UAVs). The team of agents is heterogeneous, with each UAV carrying designated sensors and all but one carry weapons as well. To successfully prosecute each target it needs to be simultaneously tracked by two UAVs and attacked by a third UAV carrying a weapon. Only for small-sized scenarios involving not more than a few vehicles and targets the problem can be solved in sufficient time using classical combinatorial optimization methods. For larger-sized scenarios the problem cannot be solved in sufficient time using these methods due to timing constraints on the simultaneous tasks and the coupling between task assignment and path planning for each UAV. A genetic algorithm (GA) is proposed for efficiently searching the space of feasible solutions. A matrix representation of the chromosomes simplifies the encoding process and the application of the genetic operators. To further simplify the encoding, the chromosome is composed of sets of multiple genes, each corresponding to the entire set of simultaneous assignments on each target. Simulation results show the viability of the proposed assignment algorithm for different sized scenarios. The sensitivity of the performance to variations in the GA tuning parameters is also investigated.     

合作博弈的粒子群算法求解

随着局中人人数的增加,利用传统的“占优”方法和“估值”方法进行合作博弈求解无论从逻辑上还是计算上都变得非常困难。针对此问题,将合作博弈的求解看作是局中人遵照有效性和个体理性提出分配方案,并按照一定规则不断迭代调整直至所有方案趋向一致的过程。依据该思路,对合作博弈粒子群算法模型进行构建,确定适应度函数,设置速度公式中的参数。通过算例分析,利用粒子群算法收敛快、精度高、容易实现的特点,可以迅速得到合作博弈的唯一分配值,这为求解合作博弈提供了新的方法和工具。     

Centralized and distributed cooperative Receding Horizon control of autonomous vehicle missions

We consider a setting where multiple vehicles form a team cooperating to visit multiple target points and collect rewards associated with them. The team objective is to maximize the total reward accumulated over a given time interval. Complicating factors include uncertainties regarding the locations of target points and the effectiveness of collecting rewards, differences among vehicle capabilities, and the fact that rewards are time-varying. We present a Receding Horizon (RH) control scheme which dynamically determines vehicle trajectories by solving a sequence of optimization problems over a planning horizon and executing them over a shorter action horizon. A key property of this scheme is that the trajectories it generates are stationary, in the sense that they ultimately guide vehicles to target points, even though the controller is not designed to perform any discrete point assignments. The proposed scheme is centralized and it induces a cooperative behavior. We subsequently develop a distributed cooperative controller which does not require a vehicle to maintain perfect information on the entire team and whose computational cost is scalable and significantly lower than the centralized case, making it attractive for applications with real-time constraints. We include simulation-based comparisons between the centralized algorithm and the distributed version, which illustrate the effectiveness of the latter.     

Asynchronously parallel optimization solver for finding multiple minima

We propose and analyze an asynchronously parallel optimization algorithm for finding multiple, high-quality minima of nonlinear optimization problems. Our multistart algorithm considers all previously evaluated points when determining where to start or continue a local optimization run. Theoretical results show that when there are finitely many minima, the algorithm almost surely starts a finite number of local optimization runs and identifies every minimum. The algorithm is applicable to general optimization settings, but our numerical results focus on the case when derivatives are unavailable. In numerical tests, a Python implementation of the algorithm is shown to yield good approximations of many minima (including a global minimum), and this ability is shown to scale well with additional resources. Our implementation’s time to solution is shown also to scale well even when the time to perform the function evaluation is highly variable. An implementation of the algorithm is available in the libEnsemble library at https://github.com/Libensemble/libensemble.     

A bisection-extreme point search algorithm for optimizing over the efficient set in the linear dependence case

The algorithms and algorithmic ideas currently available for globally optimizing linear functions over the efficient sets of multiple objective linear programs either use nonstandard subroutines or cannot yet be implemented for lack of sufficient development. In this paper a Bisection-Extreme Point Search Algorithm is presented for globally solving a large class of such problems. The algorithm finds an exact, globally-optimal solution after a finite number of iterations. It can be implemented by using only well-known pivoting and optimization subroutines, and it is adaptable to large scale problems or to problems with many local optima.     

Reliability analysis and optimization of weighted voting systems with continuous states input

Weighted voting systems are widely used in many practical fields such as target detection, human organization, pattern recognition, etc. In this paper, a new model for weighted voting systems with continuous state inputs is formulated. We derive the analytical expression for the reliability of the entire system under certain distribution assumptions. A more general Monte Carlo algorithm is also given to numerically analyze the model and evaluate the reliability. This paper further proposes a reliability optimization problem of weighted voting systems under cost constraints. A genetic algorithm is introduced and applied as the optimization technique for the model formulated. A numerical example is then presented to illustrate the ideas.     

Reference sharing: a new collaboration model for cooperative coevolution

Cooperative coevolutionary algorithms have been a popular and effective learning approach to solve optimization problems through problem decomposition. However, their performance is highly sensitive to the degree of problem separability. Different collaboration mechanisms usually have to be chosen for particular problems. In the paper, we aim to design a collaboration model that can be successfully applied to a wide range of problems. We present a novel collaboration mechanism that offers this type of potential, along with a new sorting strategy for individuals that are assigned multiple fitness values. Furthermore, we demonstrate and analyze our algorithm through comparison studies with other popular cooperative coevolutionary models on a suite of standard function optimization problems.     

On the role of continuously differentiable exact penalty functions in constrained global optimization

The aim of this paper is to show that the new continuously differentiable exact penalty functions recently proposed in literature can play an important role in the field of constrained global optimization. In fact they allow us to transfer ideas and results proposed in unconstrained global optimization to the constrained case.First, by drawing our inspiration from the unconstrained case and by using the strong exactness properties of a particular continuously differentiable penalty function, we propose a sufficient condition for a local constrained minimum point to be global.Then we show that every constrained local minimum point satisfying the second order sufficient conditions is an attraction point for a particular implementable minimization algorithm based on the considered penalty function. This result can be used to define new classes of global algorithms for the solution of general constrained global minimization problems. As an example, in this paper we describe a simulated annealing algorithm which produces a sequence of points converging in probability to a global minimum of the original constrained problem.     

Concurrent stochastic methods for global optimization

The global optimization problem, finding the lowest minimizer of a nonlinear function of several variables that has multiple local minimizers, appears well suited to concurrent computation. This paper presents a new parallel algorithm for the global optimization problem. The algorithm is a stochastic method related to the multi-level single-linkage methods of Rinnooy Kan and Timmer for sequential computers. Concurrency is achieved by partitioning the work of each of the three main parts of the algorithm, sampling, local minimization start point selection, and multiple local minimizations, among the processors. This parallelism is of a coarse grain type and is especially well suited to a local memory multiprocessing environment. The paper presents test results of a distributed implementation of this algorithm on a local area network of computer workstations. It also summarizes the theoretical properties of the algorithm.Research supported by AFOSR grant AFOSR-85-0251, ARO contract DAAG 29-84-K-0140, NSF grant DCR-8403483, and NSF cooperative agreement DCR-8420944.