基于自适应变异的多目标鸽群优化的无人机目标搜索

无人机在搜索任务中起着关键的作用,它能够在复杂环境中寻找到目标.无人机搜索问题是一个相对复杂的多约束条件下的多目标优化问题.大多数搜索算法不能满足搜索过程中高效率和低功耗的要求.本文所采用的目标搜索方法是一种基于Agent路由和光传感器的解耦滚动时域方法.为了优化目标搜索方法的参数,本文提出一种基于Agent路由和光传感器的自适应变异多目标鸽群优化(AMMOPIO)算法.利用自适应飞行机制可以获得较好的鸽群分布,种群具有多样性和收敛性.利用变异机制简化了鸽群优化算法中的模型,提高了搜索效率.实验仿真结果验证了所提出的AMMOPIO算法在目标搜索问题中的可行性和有效性.     

针对运动目标的多无人机协同鸽群优化搜索方法

针对多无人机协同运动目标搜索问题,本文设计了改进鸽群优化算法的协同搜索决策.首先,基于运动目标的独立性,建立了服从正态分布的目标概率信息图模型;为了提高环境中目标存在的确定度,建立了搜索环境的确定度信息图.其次,通过建立的吸引和排斥数字信息素图,引导无人机向未搜索区域飞行,减少重复搜索概率,提高协同目标搜索效率,并基于传统的鸽群算法,通过加入速度更新修正机制和精英代机制对其进行改进.然后,结合环境中目标的存在概率信息以及无人机搜索目标的探测信息,使用改进鸽群优化算法,规划无人机的最优搜索飞行路径.并设计避碰机制,以有效防止无人机搜索过程中的碰撞.最后,通过比较仿真实验验证了改进鸽群优化算法对运动目标协同搜索的有效性.     

异构网络中基于鸽群优化算法的D2D资源分配机制

针对异构蜂窝网络中D2D(device-to-device)通信用户复用蜂窝用户上行信道产生的频谱资源分配优化问题,提出一种基于改进离散鸽群算法(PIO)的D2D通信资源分配机制.通过设置信干噪比(SINR)门限值保证用户的通信服务质量(QoS),使用基于改进地图-指南针算子和认知因子的离散鸽群算法(IMCBPIO)为D2D用户进行资源分配,并采用基于接收SINR的闭环功率控制算法动态调整用户的发送功率,以减少用户与基站以及用户与用户之间存在的干扰.仿真结果表明,所提出方案能够有效抑制异构网络中由于引入D2D用户后导致的干扰,降低通信用户的中断概率,大大提高频谱利用率和系统吞吐量.     

A hybrid particle swarm optimization algorithm for high-dimensional problems

In recent years, particle swarm optimization (PSO) emerges as a new optimization scheme that has attracted substantial research interest due to its simplicity and efficiency. However, when applied to high-dimensional problems, PSO suffers from premature convergence problem which results in a low optimization precision or even failure. To remedy this fault, this paper proposes a novel memetic PSO (CGPSO) algorithm which combines the canonical PSO with a Chaotic and Gaussian local search procedure. In the initial evolution phase, CGPSO explores a wide search space that helps avoid premature convergence through Chaotic local search. Then in the following run phase, CGPSO refines the solutions through Gaussian optimization. To evaluate the effectiveness and efficiency of the CGPSO algorithm, thirteen high dimensional non-linear scalable benchmark functions were examined. Results show that, compared to the standard PSO, CGPSO is more effective, faster to converge, and less sensitive to the function dimensions. The CGPSO was also compared with two PSO variants, CPSO-H, DMS-L-PSO, and two memetic optimizers, DEachSPX and MA-S2. CGPSO is able to generate a better, or at least comparable, performance in terms of optimization accuracy. So it can be safely concluded that the proposed CGPSO is an efficient optimization scheme for solving high-dimensional problems.     

具有通信延迟的多无人机编队飞行控制

针对固定通信拓扑下的具有时变通信延迟的多无人机(multi-UAVs)系统,在一致性协议的基础上提出了分布式的编队控制算法.利用Lyapunov-Krasovskii函数分析了时延多无人机系统的稳定性,并以线性不等式(LMI)的形式给出了系统稳定的条件.当满足稳定性条件时,编队控制算法将使系统中无人机的速度和编队队形分别渐近地收敛至期望速度和期望队形.仿真实例验证了控制算法的有效性.     

混合PSO在最小均方误差调节器中的应用

利用最小均方误差调节器来改善有限拍系统是一种常用的方法,而调节器参数的选择是非常重要的。本文在确定最小均方误差调节器的过程中设计基于模拟退火的粒子群算法对参数进行了优化,并对一具体系统进行了仿真。结果表明,混合粒子群算法比单独使用粒子群算法和模拟退火算法的效果要好,同相关文献确定参数方法相比,系统的超调量和调节时间都得到了明显的改善,验证了所提算法的有效性。     

A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm

The main aim of this work consists of proposing a new three-step adjusting approach for an improved version of PID-type fuzzy structure in order to determine its design parameters based on a novel hybrid PSO search technique called PSOSCALF, combining Sine Cosine Algorithm (SCA) and Levy Flight (LF) distribution. In addition, conventional and self-tuning controllers are designed to get a better understanding of the performance and robustness of the proposed PID-type FLC approach. At first, the proposed PID-type FLC structure is defined as an optimization problem and then the PSOSCALF algorithm is applied to resolve it systematically. Evaluation of the performance quality of the proposed fuzzy structure is accomplished based on the stabilization and tracking control of a nonlinear Inverted Pendulum (IP) system. To make a complete comparison, the performance of three other optimization techniques namely simple PSO, Differential Evolution (DE) and Cuckoo Search (CS) are examined against the hybrid PSOSCALF algorithm. The simulation results demonstrate that the proposed PSOSCALF-tuned PID-type FLC structure is able to decrease the overshoot and integral square error amounts by about 25% and 10%, respectively compared to the self-tuning controllers. Finally, for more validation, all the controllers are tested under four different disturbance scenarios. Obtained results show that the proposed PID-type FLC can better stabilize the pendulum angle under all the scenarios compared to the PID and self-tuning controllers.     

基于权重QPSO算法的PID控制器参数优化

传统的PID控制器参数优化方法容易产生振荡和较大的超调量,因此智能算法如遗传算法（SGA）和粒子群算法（PSO）被用于参数优化,弥补传统算法的不足,但是遗传算法在进化过程中收敛速度慢,粒子群算法存在易于早熟的缺点。在分析量子粒子群算法（QPSO）的基础上,在算法中引入了权重系数,提出使用改进的量子粒子群算法（WQPSO）优化PID控制器参数。将改进量子粒子群算法与量子粒子群算法、粒子群算法通过benchmark测试函数进行了比较。最后,通过三个传递函数实例,分别使用Z-N、GA、PSO方法和改进的量子粒子群算法进行了PID控制器参数优化设计,并对结果进行了分析。     

基于输入约束一致性算法的多无人机编队控制

针对多无人机系统的编队控制问题,提出了一种基于级联系统理论及输入约束一致性算法的控制方法。首先,给出了垂直起降无人机系统的一般性模型。然后,基于级联系统理论将复杂的一般性模型化简成级联形式,针对级联形式模型,利用双曲正切函数的有界性质,在控制器设计时引入双曲正切函数设计了一致性控制算法。最后基于所设计的输入约束一致性控制算法,设计编队控制算法研究了多无人机编队控制问题。基于Matlab仿真平台对所提控制方法进行验证,仿真结果表明在所设计的一致性控制算法作用下,系统中所有的状态都能够趋于一致。基于所设计的输入约束一致性算法,所提编队控制算法可以实现空间中的无人机保持指定的编队队形飞行。     

Distributed Three‐Dimensional Formation Containment Control of Multiple Unmanned Aerial Vehicle Systems

This paper concentrates the distributed formation containment problems for multiple unmanned aerial vehicle (UAV) systems under both fixed directed and switching directed topologies. The objective is to introduce the formation control into the containment control research, where master UAVs should exchange information with each other to achieve and maintain a desired formation. Then, two different control protocols are proposed for the master UAVs and slave UAVs, respectively. Utilizing the algebraic graph theory and stability theory, some sufficient conditions are derived to guarantee the master UAVs complete a prespecified formation, while the states of the slave UAVs converge to a convex hull formed by those of the master UAVs. Finally, some numerical simulations are provided to verify the effectiveness of the theoretical results.     

Collision-free formation generation and tracking of unmanned aerial vehicles based on physicomimetics approach

This paper investigates the problem of collision-free leader–follower formation generation and tracking of multiple fixed-wing unmanned aerial vehicles (UAVs). A group of UAVs, described by unicycle-type models subject to velocity constraints, are required to form a desired formation, while tracking a virtual leader and achieving collision-free flight. To handle this problem, a novel control law based on physicomimetics approach is proposed, which integrates the formation generation, formation tracking, and collision avoidance together. Physical forces are imitated to design artificial forces used in control laws that drive multiple UAVs to accomplish desired collaborative behaviors. Further, the virtual repulsion is embedded in the physicomimetics-based control scheme to achieve obstacle avoidance naturally. The artificial forces have similar meaning to the physical forces because of similar forms, which facilitates the design and adjustment of the control strategy. Specially, to deal with the speed constraints of fixed-wing UAVs, a saturation function is applied to modify the control laws and the stability is proved theoretically. Finally, numerical simulations and hardware-in-the-loop experiments are provided to verify the effectiveness of the proposed control scheme.     

CAD系统的二次开发在设计优化中的应用

多学科设计优化(MDO)已经由单一的优化方法工具向集成有优化器技术,不同学科代码,能实现不同学科分析代码间通信的接口以及MDO自动化进行的优化体系工具箱发展。目前MDO框架的开发中存在的一个主要问题是分析对象的几何描述过于简单,造成不同学科分析模块间计算信息很难转换。从建立一个三维运输机参数模型入手,通过CAD系统的二次开发,将模型集成到设计优化框架中,为各学科所有的分析和相关工程任务提供一个公共分析平台和基础。     

基于自适应线性调节抗干扰PSO的光伏群体MPPT控制

光伏发电已成为新能源发电的主要研究方向,但当外界环境发生突变或由于遮挡使光伏阵列出现阴影时,传统的最大功率点跟踪(MPPT)算法会出现误判或因陷入局部最大功率点等问题而失效。针对这些问题,提出了一种自适应线性调节的粒子群(PSO)算法,并采用一个MPPT控制器同时实现多支路光伏阵列群体MPPT控制。最后,通过仿真验证所提控制策略的有效性。结果表明,自适应线性调节PSO群控方法振荡小,可实时精准跟踪最大功率点,控制电路较为简单,降低系统控制成本。     

A Layered-Encoding Cascade Optimization Approach to Product-Mix Planning in High-Mix–Low-Volume Manufacturing

High-mix-low-volume (HMLV) production is currently a worldwide manufacturing trend. It requires a high degree of customization in the manufacturing process to produce a wide range of products in low quantity in order to meet customers' demand for more variety and choices of products. Such a kind of business environment has increased the conversion time and decreased the production efficiency due to frequent production changeover. In this paper, a layered-encoding cascade optimization (LECO) approach is proposed to develop an HMLV product-mix optimizer that exhibits the benefits of low conversion time, high productivity, and high equipment efficiency. Specifically, the genetic algorithm (GA) and particle swarm optimization (PSO) techniques are employed as optimizers for different decision layers in different LECO models. Each GA and PSO optimizer is studied and compared. A number of hypothetical and real data sets from a manufacturing plant are used to evaluate the performance of the proposed GA and PSO optimizers. The results indicate that, with a proper selection of the GA and PSO optimizers, the LECO approach is able to generate high-quality product-mix plans to meet the production demands in HMLV manufacturing environments.     

基于PSO的预测控制及在聚丙烯中的应用

输入输出受限非线性系统的预测控制问题,可以看作是一个难以直接求解的约束非线性优化问题。针对预测控制在解决此类优化问题时,存在易收敛到局部极小或者非可行解,对初始值敏感等缺点,提出了一种基于微粒群优化方法的非线性预测控制算法。采用微粒群优化算法（PSO）作为模型预测控制的滚动优化方法,在线实时求解最优控制律。将PSO与序贯二次规划（SQP）算法进行对比仿真实验,求解两个标准函数优化问题,结果表明PSO能够快速有效地求得全局最小点,而SQP则很容易陷入局部极小点。将该算法应用于丙烯聚合反应过程的温度控制中,仿真结果显示了该方法的有效性。     

WSNs覆盖的拟物力导向粒子群优化策略

针对无线传感器网络的重复覆盖和算法耗时问题,提出一种拟物力导向的粒子群覆盖优化策略。通过仿真实验对该策略进行优化性能测试,与粒子群算法、粒子进化的多粒子群算法、传统遗传算法和新量子遗传算法的优化效果相比,该策略覆盖率分别提高9.5%、1.7%、6.03%和3.71%,收敛速度分别提高23.2%、1.8%、24.5%和24.5%。结果表明该优化策略具有比上述4种算法更好的覆盖优化效果。     

A hybrid intelligent GMPPT algorithm for partial shading PV system

Maximum power extraction for PV systems under partial shading conditions (PSCs) relies on the optimal global maximum power point tracking (GMPPT) method used. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system with reduced steady-state oscillation based on improved particle swarm optimization (PSO) algorithm and variable step perturb and observe (P&O) method. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced in the basic PSO algorithm (PSO–SFLA), ensuring the differences among particles and the searching of global extremum. Furthermore, adaptive speed factor is introduced into the improved PSO to improve the convergence of the PSO–SFLA under PSCs. And then, the variable step P&O (VSP&O) method is used to track the maximum power point (MPP) accurately with the change of environment. Finally, the superiority of the proposed method over the conventional P&O method and the standard PSO method in terms of tracking speed and steady-state oscillations is highlighted by simulation results under fast variable PSCs.     

A probability matrix based particle swarm optimization for the capacitated vehicle routing problem

Particle swam optimization (PSO) is a relatively new metaheuristic that has recently drawn much attention from researchers in various optimization areas. However, application of PSO for the capacitated vehicle routing problem (CVRP) is very limited. This paper proposes a simple PSO approach for solving the CVRP. The proposed PSO approach uses a probability matrix as the main device for particle encoding and decoding. While existing research used the PSO solely for assignment of customers to routes and used other algorithms to sequence customers within the routes, the proposed approach applies the PSO approach to both simultaneously. The computational results show the effectiveness of the proposed PSO approach compared to the previous approaches.     

Human evolutionary model: A new approach to optimization

The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel computational method for solving search and optimization problems with single or multiple objectives. HEM is an intelligent evolutionary optimization method that uses consensus knowledge from experts with the aim of inferring the most suitable parameters to achieve the evolution in an intelligent way. HEM is able to handle experts’ knowledge disagreements by the use of a novel concept called Mediative Fuzzy Logic (MFL). The effectiveness of this computational method is demonstrated through several experiments that were performed using classical test functions as well as composite test functions. We are comparing our results against the results obtained with the Genetic Algorithm of the Matlab’s Toolbox, Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Particle Swarm Optimizer (PSO), Cooperative PSO (CPSO), G3 model with PCX crossover (G3-PCX), Differential Evolution (DE), and Comprehensive Learning PSO (CLPSO). The results obtained using HEM outperforms the results obtained using the abovementioned optimization methods.     

一种改进的微粒群优化算法

基于惯性权重对微粒群优化算法（Particle Swarm Optimization,PSO）优化性能的显著影响,提出了一种改变惯性权重的方法以改进PSO算法的优化性能。算法中惯性权重的动态改变是通过对其进行PSO寻优来控制的。经过对标准函数的测试计算,无论是二维还是多维的问题,这种改变惯性权重的PSO算法的寻优结果的准确度和精度均得以提高,收到了良好的效果,尤其在高维情况下,显示出算法性能得到了明显改善。