A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

This article presents a genetic algorithm approach to multi-criteria motion planning of mobile manipulator systems. For mobile robot path planning, traveling distance and path safety are considered. The workspace of a mobile robot is represented as a grid by cell decomposition, and a wave front expansion algorithm is used to build the numerical potential fields for both the goal and the obstacles. For multi-criteria position and configuration optimization of a mobile manipulator, least torque norm, manipulability, torque distribution and obstacle avoidance are considered. The emphasis of the study is placed on using genetic algorithms to search for global optimal solutions and solve the minimax problem for manipulator torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than the conventional methods.     

基于量子粒子群优化的DAG并行任务调度研究*

任务调度是网络并行计算系统的核心问题之一。在有向无环图(DAG)描述问题的基础上,提出了一种进行并行任务调度的量子粒子群优化算法。首先对DAG并行任务调度问题作出定义,并给出了优化问题的目标;然后分别讨论了问题的编码表示、解码方案、位置向量的计算方法、离散问题连续化、算法的总体流程等;最后给出算法的仿真实验情况及分析,实验结果表明,该算法有良好的全局寻优性能和快捷的收敛速度,调度效果优于遗传算法和粒子群优化算法。     

基于改进PSO算法的机动通信保障任务分配方法

针对机动通信保障问题建立任务分配模型,结合梯度下降法提出一种基于改进粒子群算法(TSPSO)的任务分配模型求解方法.在TSPSO算法中增加判断极值陷阱、粒子二次搜索、设定禁忌区域、粒子淘汰与生成4个部分,并将TSPSO算法与其他4种改进PSO算法应用于四种典型测试函数的优化.结果表明,TSPSO算法收敛精度更高、收敛速度更快.在基于TSPSO算法的任务分配模型求解方法中,基于各机动通信保障单元到不同通信地点分配概率的思想对粒子群进行编码和解码,提高模型求解效率.仿真结果表明,TSPSO算法能够快速寻找到机动通信保障任务最优分配方案.     

云环境下的一种并行任务划分方法研究

并行任务划分一直是高性能计算的研究重点。结合地震资料数据处理的应用云环境,以任务运行时间估计模型作为优化目标函数,提出了一种改进的粒子群优化算法,用以解决地震资料任务划分问题。仿真实验证明,改进后的算法增强了全局搜索能力,提高了收敛速度和收敛精度,有效提高了云环境下任务的执行效率。     

Optimal redundancy resolution of a kinematically redundant manipulator for a cyclic task

This article proposes a method for the global optimization of redundancy over the whole task period in a kinematically redundant manipulator. The necessary conditions based on the calculus of variations for integral-type criteria result in a second-order differential equation. For a cyclic task, the boundary conditions for conservative joint motions are discussed. Then, we reformulate a two-point boundary value problem to an initial value adjustment problem and suggest a numerical search method based on the iterative optimization for providing a globally optimal solution using the gradient projection method. Since the initial joint velocity is parameterized with the number of redundancy, we only search parameter values in the parameterized space using the configuration error between the initial and final time. We show through numerical examples that multiple nonhomotopic extremal solutions satisfying periodic boundary conditions exist according to initial joint velocities for the same initial configuration. Finally, we discuss an algorithm for topological liftings of the paths and demonstrate the generality of the proposed method by considering the dynamics of a manipulator.     

CGSA scheduler: A multi-objective-based hybrid approach for task scheduling in cloud environment

In cloud computing task scheduling is one of the important processes. The key problem of scheduling is how to allocate the entire task to a corresponding virtual machine while maximizing profit. The main objective of this paper is to execute the entire task with low cost, less resource use, and less energy consumption. To obtain the multi-objective function for scheduling, in this paper we propose a hybridization of cuckoo search and gravitational search algorithm (CGSA). The vital design of our approach is to exploit the merits of both cuckoo search (CS) and gravitational search algorithms (GSA) while avoiding their drawbacks. The performance of the algorithm is analyzed based on the different evaluation measures. The algorithms like GSA, CS, Particle swarm optimization (PSO), and genetic algorithm (GA) are used as a comparative analysis. The experimental results show that our proposed algorithm achieves the better result compare to the existing approaches.     

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.     

Goal state optimization algorithm considering computational resource constraints and uncertainty in task execution time

A search methodology with goal state optimization considering computational resource constraints is proposed. The combination of “an extended graph search methodology” and “parallelization of task execution and online planning” makes it possible to solve the problem. The uncertainty of the task execution time is also considered. The problem can be solved by utilizing a random-based and/or a greedy-based graph-searching methodology. The proposed method is evaluated using a rearrangement problem of 20 movable objects with uncertainty in the task execution time, and the effectiveness is shown with simulation results.     

基于蚁群-遗传融合框架的仓储群机器人任务分配

将智能仓储中的自主移动群机器人订单任务分配,建模成群机器人协同调度的多目标优化问题,将成员机器人完成拣货任务的路径代价和时间代价作为优化目标.设计了蚁群-遗传算法融合框架并在其中求解.该框架中,蚁群算法作为副算法,用于初始种群优化;遗传算法改进后作为主算法.具体地,在遗传算法轮盘赌选择算子后引入精英保留策略,并在遗传操作中加入逆转算子.针对不同数量的订单任务,使用不同规模的群机器人系统进行了任务分配仿真实验.结果表明,在本文所提的融合框架中求解,较分别使用蚁群算法或遗传算法单独求解,性能上具有明显优势,能够发挥蚁群算法鲁棒性好和遗传算法全局搜索能力强的特点,提高智能仓储系统的整体运行效率.     

基于粒子群优化的异构多处理器任务调度算法

为提高异构多处理器任务调度的执行效率,充分发挥多处理器并行性能,提出一种基于粒子群优化的异构多处理器任务调度算法——FPSOTTS算法。该算法以求得任务最短完成时间为目标,首先通过建立新的编码方式和粒子更新公式实现粒子搜索空间到离散空间的映射,使连续的粒子群优化算法适用于离散的异构多处理器任务调度问题;同时通过引入禁忌算法进行局部搜索,克服粒子群算法的早熟收敛现象,避免陷入局部最优。实验结果表明,FPSOTTS算法的执行效率优于Min-min算法和遗传算法,有效地降低任务的执行时间。FP-SOTTS算法很好地解决了异构多处理器任务调度问题,并且适合于大规模并行任务调度。     

A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints

This paper addresses a stochastic assembly line balancing problem with flexible task times and zoning constraints. In this problem, task times are regarded as interval variables with given lower and upper bounds. Machines can compress processing times of tasks to improve the line efficiency, but it may increase the equipment cost, which is defined via a negative linear function of task times. Thus, it is necessary to make a compromise between the line efficiency and the equipment cost. To solve this problem, a bi-objective chance-constrained mixed 0–1 programming model is developed to simultaneously minimize the cycle time and the equipment cost. Then, a hybrid Particle swarm optimization algorithm is proposed to search a set of Pareto-optimal solutions, which employs the simulated annealing as a local search strategy. The Taguchi method is used to investigate the influence of parameters, and accordingly a suitable parameter setting is suggested. Finally, the comparative results show that the proposed algorithm outperforms the existing algorithms by obtaining better solutions within the same running time.     

Inverse kinematics and planning/control co-design method of redundant manipulator for precision operation: Design and experiments

In this paper, we proposed two novel algorithms to improve the operating accuracy and operating efficiency of the 7-DoF redundant manipulator. Firstly, an improved adaptive particle swarm optimization (APSO) algorithm is proposed to improve the solution precision and solution speed of the inverse kinematics of the 7-DoF redundant manipulator by introducing the probability transfer mechanism and the quality evaluation criterion. Meanwhile, the velocity directional manipulability measure (VDM) is introduced as an optimization index to search for the singular-free configuration with the optimal motion performance. Then, in order to further improve the execution efficiency and stability of the 7-DoF redundant manipulator, a novel planning/control co-design (PCC) algorithm is proposed based on the Dynamic Movement Primitives (DMPs-PCC), which ensures that the motion planner and actuator of the 7-DoF redundant manipulator can work synchronously, while optimizing the velocity and acceleration profiles of each joint of the manipulator in the operating process. Finally, an experimental platform is established based on the Robot Operating System (ROS), and the effectiveness and reliability of the two novel algorithms are demonstrated by the simulations and prototype experiments.     

Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization

In the present paper, particle swarm optimization, a relatively new population based optimization technique, is applied to optimize the multidisciplinary design of a solid propellant launch vehicle. Propulsion, structure, aerodynamic (geometry) and three-degree of freedom trajectory simulation disciplines are used in an appropriate combination and minimum launch weight is considered as an objective function. In order to reduce the high computational cost and improve the performance of particle swarm optimization, an enhancement technique called fitness inheritance is proposed. Firstly, the conducted experiments over a set of benchmark functions demonstrate that the proposed method can preserve the quality of solutions while decreasing the computational cost considerably. Then, a comparison of the proposed algorithm against the original version of particle swarm optimization, sequential quadratic programming, and method of centers carried out over multidisciplinary design optimization of the design problem. The obtained results show a very good performance of the enhancement technique to find the global optimum with considerable decrease in number of function evaluations.     

基于分区间强化学习的集群导弹快速任务分配EI北大核心CSCD

针对集群导弹在线任务分配面临的环境不确定、耗时过长等问题,本文研究了一种基于分区间强化学习的集群导弹快速任务分配算法.首先,建立集群导弹的综合攻防性能模型,并将存在环境不确定性的集群导弹任务分配问题表述为马尔可夫决策过程.其次,针对该过程采用分区间强化学习,通过将搜索空间划分成若干个子区间,降低搜索维度,加快算法的收敛过程,并通过理论证明给出了最优区间划分依据.最后,通过3组仿真实验,分别从收敛速度、不确定条件下的寻优能力以及导弹和目标数量可变情况下的决策能力3个方面,验证了所提算法的快速性和优化性能.     

一种时变洋流场下AUV最优能耗路径规划方法

在时变洋流场环境下,洋流矢量增加了时间维度,在时间角度上可进一步利用洋流以节约自主水下机器人(AUV)能量消耗.此外,在该环境中无后效性不再成立,基于经典贪婪策略的路径规划算法不再适用.鉴于此,结合路径参数选择和双层规划算法,提出一种适用于时变洋流场环境的能耗最优路径规划算法.出发时间和AUV推进速度均可以在时间维度上等待有利洋流,且推进速度与其能量消耗直接相关,因此,引入出发时间和推进速度作为路径参数.在此基础上,针对无后效性不成立问题,使用双层规划作为路径规划算法,分析该算法在时变洋流场环境下的适用性.算法将路径规划任务分为路径规划与路径优化两部分,路径规划部分采用蚁群系统算法构建通道,路径优化部分由量子粒子群算法对路径参数进一步优化,在保证全局最优的同时能够解决传统基于栅格的路径规划算法中机器人运动方向受限的问题.最后以Kongsberg/Hydroid REMUS 600s型水下机器人为模型,对所提出的路径规划算法进行仿真验证.     

A pattern based PSO approach for block matching in motion estimation

Block matching motion estimation is a popular method in developing video coding applications. A new algorithm has been proposed for reducing the number of search points using a pattern based particle swarm optimization (PSO) for motion estimation. The conventional particle swarm optimization has been modified to provide accurate solutions in motion estimation problems. This leads to very low computational cost and good estimation accuracy. Due to the center biased nature of the videos, the proposed approach uses an initial pattern to speed up the convergence of the algorithm. Simulation results show that improvements over other fast block matching motion estimation algorithms could be achieved with 31%～63% of search point reduction, without degradation of image quality.     

一种基于博弈策略的群智能属性约简算法

建立了粒子群算法与博弈论之间的联系,在此基础上,引入一种基于博弈策略的群智能搜索机制,并应用于粗糙集最小属性约简问题的求解。由此构建的属性约简算法,可以设置不同的参与团体及其博弈策略,构建相应的支付效用矩阵,并能通过博弈过程构建策略的最优组合。多个UCI数据集的实验计算表明提出的基于博弈策略的新算法求解质量优于粒子群优化算法、禁忌搜索、遗传变异和变异粒子群优化算法,并具有较小的计算开销。     

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.     

A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing

Scheduling means devoting tasks among computational resources, considering specific goals. Cloud computing is facing a dynamic and rapidly evolving situation. Devoting tasks to the computational resources could be done in numerous different ways. As a consequence, scheduling of tasks in cloud computing is considered as a NP-hard problem. Meta-heuristic algorithms are a proper choice for improving scheduling in cloud computing, but they should, of course, be consistent with the dynamic situation in the field of cloud computing. One of the newest bio-inspired meta-heuristic algorithms is the chicken swarm optimization (CSO) algorithm. This algorithm is inspired by the hierarchical behavior of chickens in a swarm for finding food. The diverse movements of the chickens create a balance between the local and the global search for finding the optimal solution. Raven roosting optimization (RRO) algorithm is inspired by the social behavior of raven and the information flow between the members of the population with the goal of finding food. The advantage of this algorithm lies in using the individual perception mechanism in the process of searching the problem space. In the current work, an ICDSF scheduling framework is proposed. It is a hybrid (IRRO-CSO) meta-heuristic approach based on the improved raven roosting optimization algorithm (IRRO) and the CSO algorithm. The CSO algorithm is used for its efficiency in satisfying the balance between the local and the global search, and IRRO algorithm is chosen for solving the problem of premature convergence and its better performance in bigger search spaces. First, the performance of the proposed hybrid IRRO-CSO algorithm is compared with other imitation-based swarm intelligence methods using benchmark functions (CEC 2017). Then, the capabilities of the proposed scheduling hybrid algorithm (IRRO-CSO) are tested using the NASA-iPSC parallel workload and are compared with the other available algorithms. The obtained results from the implementation of the hybrid IRRO-CSO algorithm in MATLAB show an improvement in the average best fitness compared with the following algorithms: IRRO, RRO, CSO, BAT and PSO. Finally, simulation tests performed in cloud computing environment show improvements in terms of reduction of execution time, reduction of response time and the increase in throughput by using the proposed hybrid IRRO-CSO approach for dynamic scheduling.     

电能表检验台体串联机械臂时间最优轨迹规划

时间最优轨迹规划有助于缩短机械臂运动时间,提高工作效率,在机械臂实际应用场景中起着至关重要的作用.针对串联机械臂点到点运动的时间最优轨迹规划问题,提出一种基于改进多种群遗传算法的最优轨迹规划方法.通过五次多项式插值对机械臂运动路径进行拟合,利用改进的多种群遗传算法对机械臂运动时间进行优化,改进之处包括:设计含有惩罚项的...