An Evolutionary Approach to Dynamic Task Scheduling on FPGAs with Restricted Buffer

Dynamically and partially reconfigurable field-programmable gate arrays (FPGAs) allow to swap in and out tasks without interrupting the execution of other tasks. The FPGA controller can decide on-line where to place new tasks onto the FPGA. Rearranging a subset of the tasks executing on the FPGA may allow the next pending task to be processed sooner. When tasks are rearranged, the arriving input data have to be buffered while the execution is suspended. In this paper, we describe and evaluate an evolutionary approach to solve the problem of placing and rearranging tasks that are supplied by input streams which have constant data rates. We use two genetic algorithms (GAs): one for identifying feasible rearrangements and the other for scheduling a selected rearrangement so that the delay caused by this rearrangement is small and the limited input buffer size is respected.     

An Evolutionary Approach to Multiobjective Clustering

The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature. The conceptual advantages of the multiobjective formulation are discussed and an evolutionary approach to the problem is developed. The resulting algorithm, multiobjective clustering with automatic k-determination, is compared with a number of well-established single-objective clustering algorithms, a modern ensemble technique, and two methods of model selection. The experiments demonstrate that the conceptual advantages of multiobjective clustering translate into practical and scalable performance benefits     

粒子群算法的进化式实时优化方法

针对传统实时优化方法需要等待稳态以及模型更新和优化计算复杂等缺点,提出了一种进化式实时优化方法.该方法将模型更新与控制器设定点优化计算表达为一个多目标的进化过程,把扰动后等待稳态的过程划分为若干个“拟稳态”区间,并根据测量值进行连续的模型更新和优化计算.采用粒子群多目标优化算法对模型参数和操作参数进行统一编码和进化操作,详细给出了算法的实现步骤.经Williams-Otto反应器为对象进行了算法的仿真和分析,证明了该优化方法的有效性.     

Contribution to Design of Complex Mechatronic Systems. An Approach through Evolutionary Optimization

This paper describes a new evolutionary methodology aimed at optimizing various and heterogeneous data in common evolution. The representation of solutions uses mixed-integer genotypes and variable-length chromosomes to face a complex problem of task decomposition and high-level control generation. A memory operator is introduced to face convergency uncertainties issued from the irregularities of both discontinuous evaluation function and heterogeneous solution representation. The stability of the evolutionary algorithm is analyzed with dimension changes in the optimization problem.     

基于演化策略算法的作业车间调度优化

提出一种基于工件操作次序的二维实数编码方法,采用演化策略算法求解作业车间调度问题。设计一种基于三点交叉互换的重组算子用于生成子代个体,并采用个体编码基因随机重新生成的方法设计变异算子。实验结果证明,演化策略算法能有效优化作业车间调度问题,与遗传算法和粒子群优化算法相比,其优化性能更好,并且基于三点交叉互换重组算子的演化策略算法的性能好于基于两点交叉和基于四点交叉互换重组算子的演化策略算法。     

带分批优化的多级批处理过程自组织调度方法

针对一类带批次划分的多级批处理过程优化调度问题,提出一种自下而上的自组织调度方法．首先,通过构造与批处理生产过程中的订单、批次和设备相对应的自组织个体,建立自组织调度模型框架;然后,分析多级批处理调度问题的最优性质,提出分批优化规则和自组织选择策略,并在此基础上给出自组织优化调度算法;最后,通过调度实例求解结果表明,所提方法能在短时间内获得问题的最优解或近优解,进而验证了该方法的有效性和优越性．     

设备维修最优化调度策略方法研究

信息化条件下,战时装备维修优化调度问题是装备维修保障过程中的关键问题。本文根据PSO算法建立模型提出了战时装备维修保障调度策略,最大限度的提高战时维修保障系统的效能,同时对PSO算法进行改进,解决算法中的局部最优化问题,最后与基于FCFS算法的维修保障调度策略进行对比,通过仿真实验证明PSO算法对调度性能有明显改善。     

多机服务器任务调度的经济学方法q

本文针对多机服务器提出了一种任务调度的经济学方法以一般均衡理论为基础,依靠价格机制实现资源的优化分配;讨论了多机服务器的系统模型、任务的聚类和效用函数、经济学模型及均衡状态的最优性;最后通过模拟实验验证了经济学方法的有效性。     

项目优化调度的多智能体社会进化算法

结合多智能体系统、进化算法以及关系网模型,提出了一种多智能体社会进化算法用于求解项目活动的一个最优调度顺序以使整个工程的工期最短,每个智能体生存于环境中,为了增加自身能量将与其邻域展开竞争及协同操作,同时可利用自身的知识进行自学习来增加能量,根据项目优化调度的问题特点,设计了智能体的竞争行为、协同行为以及自学习行为,通过对PSPLIB中的标准问题进行测试,同时与其他启发式算法相比较的仿真实验结果表明该算法具有良好的性能,能在较短的时间内寻找到十分接近"最优解"的调度序列.     

一种求解约束优化问题的演化规划算法

提出了一种新的求解约束优化问题的演化算法——基于混合策略求解约束优化问题的演化规划算法(CMSEP).借鉴了Mezura-Montes的算法中直接比较的约束处理方法,为求解位于边界附近的全局最优解采用多样性保护机制,允许一定比例最好不可行解进入下一代种群,混合策略变异机制用于指导算法快速搜索过程.标准测试函数的实验结果验证了算法的通用性和有效性.     

Resilience: An Evolutionary Approach to Spatial Economic Systems

The concept of resilience has received a great deal of attention in the past decades. Starting from the first fundamental definitions offered by Holling, Pimms and Perrings in an economic-ecological modeling context, the present paper explores the evolution of the resilience concept—as well as related different measures—in both a continuous and discrete time setting.From this perspective, the paper explores the relevance of the resilience concept in socio-economic systems, by focussing the attention on the relationships among resilience, transition dynamics and lock-in effects, in particular in the light of the dynamics of technological innovation diffusion and adaptive behaviour of firms. In this framework we will describe an empirical application, in which the resilience and dynamics of the West-German labour market will be investigated. This empirical illustration is offered by making use of an algorithm constructed for detecting Lyapunov exponents, so as to classify the resilience among employment sectors in our case study.     

多机服务器任务调度的经济学方法

本文针对多机服务器提出了一种任务调度的经济学方法 :以一般均衡理论为基础 ,依靠价格机制实现资源的优化分配 ;讨论了多机服务器的系统模型、任务的聚类和效用函数、经济学模型及均衡状态的最优性 ;最后通过模拟实验验证了经济学方法的有效性     

An Optimization Approach to Control Reconfiguration

An optimization algorithm for the problem of control reconfiguration in response to operating condition changes or abrupt system component failures is presented here. The algorithm utilizes a version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method of conjugate directions. The algorithm provides an output feedback controller that not only stabilizes the new /impaired system, when possible, but also preserves much of the dynamics of the original /unfailed system. The design is such that the closed-loop system is robust with respect to inevitable uncertainties /modelling errors on the state-space matrices of the impaired system. The algorithm is applied to an aircraft longitudinal control system, for which two severe cases of failure are considered, first the loss of an actuator and then the loss of a sensor in addition to the actuator loss.     

基于资源约束的并行活动优化调度方法

针对并行产品开发过程,提出了一种基资源约束的优化调度算法,首先给出了并行活动的三个定义,提出了一个使算法能够实现优化调度的综合函数,并研究了并行工程中的两个关键问题－－角色分配和反馈修改,仿真实例表明了算法的可行性。     

An Integrated Approach to Scheduling in Safety-Critical Embedded Control Systems

This paper describes an approach that has been developed over a number of years for the job of scheduling systems and providing evidence that timing requirements are met. The approach has been targeted at the safety-critical systems domain, and more specifically the development of control systems for jet engines. The work provides a usable computational model that supports the reuse of legacy systems. In addition, timing analysis has been developed that features low pessimism, low computational complexity and that is robust to change. The contributions of this paper are to show how standard timing analysis is often insufficient for real systems, presenting extensions to the standard analysis to give an integrated approach to verification, and providing a case study that demonstrates the appropriateness and benefits of the overall technique.     

An Endosymbiotic Evolutionary Algorithm for Optimization

This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.     

一种基于Alopex的进化优化算法

提出一种基于Alopex的进化算法.该算法在迭代过程中从种群中随机选择两个个体,通过计算两个个体自变量和目标函数值的变化情况确定算法进一步搜索方向的概率,逐步迭代最终收敛到全局最优.该算法具备基本进化算法和Alopex算法的优点,在一定程度上具有梯度下降法和模拟退火算法的优点.通过基准函数的测试和反应动力学参数估计的应用表明,该算法的全局搜索能力有了显著提高.特别是对多峰函数能够有效避免早熟收敛问题.     

An Optimization-Based Approach for Design Project Scheduling

Concurrent engineering has been widely used in managing design projects to speed up the design process by concurrently performing multiple tasks. Since the progress of a design task often depends on the knowledge about other tasks and requires effective communication, tasks and communication activities need to be properly coordinated to avoid delays caused by waiting for information or the need for rework. This paper presents a novel formulation for design project scheduling with explicit modeling of task dependencies and the associated communication activities. General dependencies are modeled as combinations of three basic types representing sequential, concurrent, and independent processes. Communication activities are also modeled as tasks, and their interactions with design tasks are described by sets of intertask constraints. The objective is to achieve timely project completion with limited resources. To improve algorithm convergence and schedule quality, penalties on the violation of constraints coupling design tasks are added to the objective function. A solution methodology that combines Lagrangian relaxation, dynamic programming, and heuristic is developed to schedule design and communication tasks, and a surrogate optimization framework is used to overcome the “inseperability” caused by nonadditive penalties. A heuristic procedure is then developed to obtain scheduling policies from optimization results and to dynamically construct schedules. Numerical results show that the approach is effective to handle various task dependencies and the associated communication activities to provide high-quality schedules.      

An Agent-Based Approach for Scheduling Multiple Machines

We present a new agent-based solution approach for the problem of scheduling multiple non-identical machines in the face of sequence dependent setups, job machine restrictions, batch size preferences, fixed costs of assigning jobs to machines and downstream considerations. We consider multiple objectives such as minimizing (weighted) earliness and tardiness, and minimizing job-machine assignment costs. We use an agent-based architecture called Asynchronous Team (A-Team), in which each agent encapsulates a different problem solving strategy and agents cooperate by exchanging results. Computational experiments on large instances of real-world scheduling problems show that the results obtained by this approach are significantly better than any single algorithm or the scheduler alone. This approach has been successfully implemented in an industrial scheduling system.     

多进程动态调度方法

文章对相关进程的典型关系进行了描述,并讨论了进程中有序和同步问题的实现方法。