Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Cooperative Search of UAV Swarm Based on Ant Colony Optimization with Artificial Potential Field

An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.

Coalition Formation for Multiple UAVs Cooperative Search and Attack with Communication Constraints in Unknown Environment

A coalition formation algorithm is presented with limited communication ranges and delays in unknown environment,for the performance of multiple heterogeneous unmanned aerial vehicles(UAVs)in cooperative search and attack missions.The mathematic model of coalition formation is built on basis of the minimum attacking time and the minimum coalition size with satisfying resources and simultaneous strikes requirements.A communication protocol based on maximum number of hops is developed to determine the potential coalition members in dynamic network.A multistage sub-optimal coalition formation algorithm(MSOCFA)with polynomial time is established.The performances of MSOCFA and particle swarm optimization(PSO)algorithms are compared in terms of complexity,mission performance and computational time.A complex scenario is deployed to illustrate how the coalitions are formed and validate the feasibility of the MSOCFA.The effect of communication constraints(hop delay and max-hops)on mission performance is studied.The results show that it is beneficial to determine potential coalition members in a wide and deep range over the network in the presence of less delay.However,when the delays are significant,it is more advantageous to determine coalitions from among the immediate neighbors.

A Cooperative Schema between Web Sever and Search Engine for Improving Freshness of Web Repository

Because the web is huge and web pages are updated frequently, the index maintained by a search engine has to refresh web pages periodically. This is extremely resource consuming because the search engine needs to crawl the web and download web pages to refresh its index, Based on present technologies of web refreshing, we present a cooperative schema between web server and search engine for maintaining freshness of web repository. The web server provides metadata defined through XML standard to describe web sites. Before updating the web page the crawler visits the meta-data files. If the meta-data indicates that the page is not modified, then the crawler will not update it. So this schema can save bandwidth resource. A primitive model based on the schema is implemented. The cost and efficiency of the schema are analyzed.

Adaptive Fractional-Order PID Control for VSC-HVDC Systems via Cooperative Beetle Antennae Search with Offshore Wind Integration

Since the voltage source converter based high voltage direct current(VSC-HVDC)systems owns the features of nonlinearity,strong coupling and multivariable,the classical proportional integral(PI)control is hard to obtain content control effect.Hence,a new perturbation observer based fractional-order PID(PoFoPID)control strategy is designed in this paper for(VSC-HVDC)systems with offshore wind integration,which can efficiently boost the robustness and control performance of entire system.Particularly,it employs a fractional-order PID(FoPID)fra-mework for the sake of compensating the perturbation estimate,which dramatically boost the dynamical responds of the closed-loop system,and the cooperative beetle antennae search(CBAS)algorithm is adopted to quickly and effi-ciently search its best control parameters.Besides,CBAS algorithm is able to efficiently escape a local optimum because of a suitable trade-off between global exploration and local exploitation can be realized.At last,comprehensive case studies are carried out,namely,active and reactive power tracking,5-cycle line-line-line-ground(LLLG)fault,and offshore wind farm integration.Simulation results validate superiorities and effectiveness of PoFoPID control in com-parison of that of PID control and feedback linearization sliding-mode control(FLSMC),respectively.

非合作博弈背景下基于BSA的配电网优化重构

为缓解分布式电源大规模接入对配电网安全稳定运行的影响,提出一种考虑分布式电源输出功率的不确定性的有源配电网优化重构方法.首先,采用非合作博弈理论研究电网调度人员与“大自然”之间的博弈关系,将配电网系统中光伏单元的不确定性视为“大自然”博弈方;其次,以有功网损、负荷均衡度、电压偏差最小为目标函数,建立有源配电网优化重构模型,通过回溯搜索算法(backtracking search algorithm,BSA)进行迭代求解,得到最优重构方案;最后,在IEEE33节点系统进行仿真分析,验证模型的正确性及求解算法的有效性.研究结果表明,相较传统重构方法,本文方法更充分考虑了分布式电源输出功率的不确定性,并且在最恶劣的情况发生时,得到的重构策略能够使配电网系统的有功网损、负荷均衡度、电压偏差指标分别降低0.31%、0.59%、0.48%.

跨域无人集群协同反潜搜索方法研究

针对海上反潜搜索问题,提出一种将无人艇作为无人机的通信中继的跨域无人集群协同搜索方法,采用数字网格地图表征任务区域,构建了跨域平台的运动学模型;提出了跨域无人系统的协作方法,设计了无人系统之间的分布式信息融合机制;设计了异构平台的搜索目标函数,引导无人系统在搜索任务中实时决策。仿真结果表明:所提方法能够有效适用于文中反潜搜索任务想定,跨域平台之间能够通过优势互补提高搜索效能。

水下人-机协同搜救作业人因可靠性分析研究

为分析水下人-机协同搜救作业人因可靠性并识别其关键影响因素,根据水下搜救训练实际和相关研究结论提取人因可靠性影响因素;建立人因可靠性影响因素体系,利用解释结构模型定性分析各影响因素间相关关系,将建立的各影响因素的解释结构模型转换为贝叶斯网络拓扑结构,分别构建潜水员作业、无人遥控潜水器(ROV)作业、人-机协同作业人因可靠性分析贝叶斯网络;通过对3类贝叶斯网络进行推理,揭示影响人因可靠性的关键因素及其依赖关系,并定量分析3种作业模式下的人因可靠性变化。结果表明:人-机协同搜救作业中“误解意图”“通信不畅”“信息不对称”“作业精度不足”“丧失救助能力”等影响因素起到关键作用;当上述因素得到较好控制时,人-机协同搜救作业的人因可靠性将会表现出优于单纯的ROV或潜水员独立施救的效能。研究结果可为水下搜救作业人因可靠性分析与提升提供参考。

Seru系统调度优化的知识引导协同进化算法

作为一种新型的生产模式, Seru系统能够兼顾柔性和效率且快速响应市场,已在装配企业得到广泛应用.为了实现实际生产过程生产效率和劳动效率的协同优化,本文研究以最小化最大完工时间和工人总劳动时间为目标的Seru系统多目标调度问题,提出一种知识引导的协同进化算法.首先,将问题分解为Seru构造和Seru调度,构造两个种群分别优化子问题.同时,设计种群规模的调整策略,通过为有潜力的种群分配更多个体来提高协同搜索的效率.进而,通过分析问题的性质,提炼规则性知识用于设计有效的搜索算子和重生成规则,指导精英个体执行知识驱动的增强搜索,从而进一步提升算法的局部开发能力.通过数值仿真和统计性能对比,验证了算法各设计环节的有效性,并取得了显著优于现有最新算法的多目标调度优化性能.

基于差分进化粒子群混合算法的多无人机协同区域搜索策略

为提高无人机群在未知环境中的区域搜索效率,提出一种多无人机协同区域搜索策略。首先,根据区域搜索任务需求,建立包含区域覆盖率、区域不确定度、目标存在概率三种属性的区域信息地图;其次,以最大化搜索效率、同时最小化无人机搜索过程中的能耗为目标,建立无人机区域搜索滚动时域优化目标函数,指导无人机在线决策搜索路线;然后针对传统群智能优化算法易陷入局部最优的缺陷,设计差分进化粒子群混合算法在线求解该多目标优化问题,提高算法的寻优性能,从而提高无人机的搜索效率。最后,通过数值仿真实验,对所提算法进行验证,仿真结果表明,文中设计的基于差分进化粒子群混合算法的多无人机协同区域搜索策略与传统的群智能优化算法相比具有更高的区域搜索效率。

面向多无人车的目标点分配和协同路径规划算法

针对多智能体路径搜索算法在非指定式多车协同路径规划问题中路径冗长,计算效率低等缺陷,提出协同目标点分配路径规划算法Nutcracker-CBS。首先构建紧耦合目标点分配MAPF框架,实现目标点分配和路径构建的联合寻优;针对目标点分配模块,提出改进的星鸦优化算法,增量式求解分配问题,缩短模块用时;针对路径构建模块,提出改进的MAPF算法,通过回退式约束构建机制,引入避碰路径估计的绕道机制和数据共享底层路径规划机制,提升效率和路径质量。数据集实验中,Nutcracker-CBS时耗相比SOTA算法减少90.37%;目标点分配模块求解耗时减少86.76%;MAPF模块6 s内构建100辆无人车路径,平均路径长度缩短6.058%。实际实验中路径总和与系统运行时长分别减少55.26%和61.29%,提升了多机器人系统的效率,降低了路径长度。

面向动态目标搜索与打击的空地协同自主任务分配方法

面向复杂城市环境中的运动目标搜索与打击任务,单域无人平台受视野范围和运动能力等限制,易出现目标遗漏和任务完成率低的问题。针对这些问题,提出一种面向动态目标搜索与打击的空地协同自主任务分配方法,通过结合无人机视野范围广以及无人车机动性强的特点,提升空地无人系统的任务执行效率和区域覆盖率。一方面,针对未知运动目标,提出一种基于数字信息素的目标搜索模型,以平台协同收益和区域覆盖率为优化指标,保证在尽可能短的时间周期内发现区域中的所有目标。另一方面,面向动态到达的打击任务,构建基于可行路径规划的任务分配模型,以平台能耗和任务完成时间为目标函数,在保证任务完成率的同时,提高空地协同系统的资源利用率。与现有方法相比,所提方法能够在最短的时间内发现所有目标,区域覆盖率达到55%以上,且资源利用率为84.4%。实验结果表明,所提方法具备较好的目标搜索和任务执行能力。

基于关联定位误差信息素的巡航导弹集群快速协同搜索研究

协同搜索是巡航导弹多弹控制领域的一个未来发展方向,对其进行研究有助于巡航导弹适应未来战场智能化作战需求。针对缺少先验环境信息下巡航导弹集群对目标的高效搜索问题,本文提出了一种基于关联定位误差的改进信息素快速协同搜索算法,筛选组内精英个体领导集群成员的搜索。在此基础上,结合交叉测向、位置控制、信息融合、自主分组等技术,构建了适用于巡航导弹集群的具体实现架构。通过数值仿真分析和对比,本文构建的架构能够实现特定场景下巡航导弹集群对多未知目标的位置定位,而提出的改进信息素搜索算法能够提升协同搜索的效率。

两阶段模型协同搜索的昂贵多目标进化优化

近年来,昂贵多目标优化问题的求解获得了越来越多的关注.然而,随着决策空间维度的升高,模型的有效性和准确性很难保证.因此,本文提出了一种两阶段模型协同搜索的昂贵多目标进化优化.在该方法中,每轮种群进化前构建全局模型,以辅助加快对最优解集的搜索.随后,利用搜索到的种群选择其邻域样本训练局部模型,对二者集成辅助算法进行进一步搜索.最后,提出基于不确定度的填充采样策略选点,进行真实评价.为了验证算法的有效性,将本文算法与4个算法分别在DTLZ和MaF测试集以及两个实际问题上进行比较,实验结果表明其具有良好的性能.

自适应策略优化的粒子群优化算法在神经网络架构搜索中的应用

针对神经网络架构搜索(NAS)任务,提出一种自适应重启策略驱动的协作学习粒子群优化(ARCLPSO)算法。算法核心流程包括协作学习与信息共享、策略切换和参数自适应,以改进传统粒子群优化(PSO)算法在NAS中的性能。ARCLPSO算法结合了全局与局部信息的协同作用和智能切换学习策略。具体地,ARCLPSO利用全局和局部信息的协同作用令粒子向更优的方向移动,通过智能的切换粒子学习策略平衡粒子的搜索性能和收敛速度,提高搜索速度和搜索质量。在NAS-Bench-101数据集上的实验结果表明,ARCLPSO的收敛时间相较于传统进化算法(REA)和随机搜索(RS),分别减少了40.9%和55.2%。

求解大规模全局优化问题的新型三层递归差分分组方法

协同进化算法在求解大规模全局优化问题上具有较好的效果,其核心思想是利用分而治之的策略将一个高维问题分解成若干个子问题,然后分别优化每个子问题.然而,现有的分解方法通常需要花费大量的计算成本来获得精确的变量分组.通过采用递归交互检测中的历史信息简化分组过程,能够避免检测某些集合的相互关系,本文提出了一种新型三层递归差分分组策略(NTRDG).与其他4种现有的分组方法相比,NTRDG在不影响分组精度的情况下计算成本消耗较低.仿真结果表明,NTRDG在求解大规模全局优化问题时具有很强的竞争力.

面向协同探测的多机雷达功率时间联合优化分配算法

针对多目标搜索及跟踪场景,研究了面向协同探测的多机雷达功率时间联合优化分配算法。首先,基于信号检测理论和克拉美-罗下界,分别推导了雷达搜索性能与跟踪性能评估指标;在此基础上,建立了面向协同探测的多机雷达功率时间联合优化分配模型,即以最大化雷达工作性能指标为优化目标,以满足给定系统资源限制为约束条件,对雷达搜索及跟踪任务中节点选择、辐射功率和任务时间等参数进行联合优化设计;最后,针对上述优化问题,采用基于内点法和粒子群算法的三步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能够在满足给定系统资源限制的条件下,有效提高雷达系统搜索性能和跟踪精度。

基于航迹消除与策略迭代的无人机集群区域目标搜索方法

无人机集群区域搜索在军事领域以及民用领域的搜救、巡逻、监测、环境勘测等方面有着广泛的应用,但如何保证不同场景下无人机集群搜索方法的效率问题依然是个难题。为了更好地解决搜索目标先验信息已知的无障碍区域内多无人机集群搜索航迹规划问题,提高无人机集群搜索效率,本文根据目标区域热度以及传感器探测概率等先验信息,提出了一种基于无人机航迹消除策略的概率计算方法,并在此基础上结合策略迭代算法动态规划无人机航迹,找到单个无人机航迹覆盖率最优策略;进而通过适当组合顺序实现无人机集群区域目标搜索整体覆盖率最优;最后,通过仿真计算验证了算法的有效性。