基于HSDPA混合群集智能算法的多用户调度

传统的基于粒子群最优化的混合启发式算法和模拟退火算法往往以牺牲解的质量或者求解速度来实现有效的调度,为了解决这一问题,提出了一种基于高速下行分组接入（HSDPA）标准的混合群集智能算法。首先假定HSDPA标准所指定的是现实性不完善的信道状态信息（CSI）反馈,并以有限集合的形式存在于信道指示符（CQI）中;接着在最优化过程中,利用模拟退火算法和粒子群最优化算法各自的优点设计混合群集智能算法;最后利用混合算法进行数据处理,得到最优解的同时降低了复杂度,从而实现提升系统通量,达到调度最优化的目的。实验结果表明,与传统的基于粒子群最优化的算法相比,所提的混合算法取得了更好的调度效果。     

改进混合粒子群算法的立体车库存取调度

为了进一步提高立体车库存取效率，提出一种改进混合粒子群算法，应用于立体车库存取策略时间模型，寻找存取车最优时间和最优排序。该算法主要在粒子群算法前期引入遗传算法，改善全局搜索能力，后期引入模拟退火算法弥补其局部搜索能力弱的特点。与目前应用于立体车库存取车调度的遗传算法相比，改进混合粒子群算法存取效率提高了24.5%~36.07%，并优于其他车库调度算法，提高了车库运营效率。     

A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

粒子群优化多处理机任务调度算法

提出基于粒子群优化的多处理机调度算法,采用列表调度,同时把粒子群的矢量表达方式转换为基于调度优先级的模型。调度结果显示能提高全局搜索能力,加快进化速度,优于模拟退火等启发式算法结果。     

一种多目标置换流水车间调度问题的优化算法

针对最大完工时间最小和总流经时间最小的多目标置换流水车间调度问题(permutation flow shopscheduling problem, PFSP), 提出一种粒子群优化算法与变邻域搜索算法结合的混合粒子群优化(hybrid particleswarm optimization algorithm, HPSO)算法, 并使算法在集中搜索和分散搜索之间达到合理的平衡. 在该混合算法中,采用NEH 启发式算法进行种群初始化, 以提高初始解质量;运用随机键表示法设计基于升序排列规则(ranked-order-value, ROV), 将连续PSO 算法应用于置换流水车间调度问题;引入外部档案集存贮Pareto 解, 并采用强支配关系和聚集距离相结合的混合策略保证解集的分布性;采用Sigma 法和基于聚集距离的轮盘赌法进行全局最优解的选择;提出变邻域搜索算法, 对外部集中的Pareto 解作进一步地局部搜索. 最后, 运用提出的混合算法求解Taillard 基准测试集, 并将测试结果与SPEA2 算法进行比较, 验证该调度算法的有效性.     

一种应用于柔性作业车间成组调度问题的分级优化算法

提出一种求解柔性作业车间成组调度FGJSS(flexible grouped job-shop scheduling)问题的蚁群粒子群求解算法。算法采用主从递阶形式,主级为蚁群优化算法,选择零件加工设备;从级为粒子群优化算法,在主级零件加工设备约束下优化设备作业排序以实现流通时间最小的目标。算法中,以工序加工时间和设备承载的作业族数为启发式信息设计蚂蚁在工序可用设备间转移概率;以粒子向量优先权值和作业族号为依据设计解码方法实现设备上的成组作业排序。最后,通过仿真实验,验证了该算法的有效性。     

Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization

Particle swarm optimization (PSO) is a novel metaheuristic, which has been applied in a wide variety of production scheduling problems. Two basic characteristics of this algorithm are its efficiency and effectiveness in providing high-quality solutions. In order to improve the traditional PSO, this study proposes the incorporation of a local search heuristic into the basic PSO algorithm. The new, hybrid, metaheuristic is called “twin particle swarm optimization (TPSO)”. The proposed metaheuristic scheme is applied to a flow shop with multiprocessors scheduling problem, which can be considered a real world case regarding the production line. This study, as far as the multiprocessors flow shop production system is concerned, utilizes sequence dependent setup times as constraints. Finally, simulated data confirm the effectiveness and robustness of the proposed algorithm. The data test results indicate that TPSO has potential to replace PSO and become a significant heuristic algorithm for similar problems.     

基于QPSO算法的作业车间调度问题的研究

针对现行的遗传算法存在进化速度过慢和过早收敛的局限,以及粒子群优化算法搜索空间有限、容易陷入局部最优点的缺陷,提出将一种基于量子行为的粒子群优化算法应用于作业车间调度问题.将该问题中的每个调度组成一个多维向量,以此向量作为量子粒子群优化算法中的粒子进行进化,由此在解空间内搜索最优解.实例仿真结果表明,该算法收敛速度快、全局收敛性能好,可以得到比遗传算法、粒子群优化算法更佳的调度效果,证明了算法的有效性.     

基于蚁群粒子群算法求解多目标柔性调度问题

通过分析多目标柔性作业车间调度问题中各目标的相互关系,提出一种主、从递阶结构的蚁群粒子群求解算法。算法中,主级为蚁群算法,在选择工件加工路径过程中实现设备总负荷和关键设备负荷最小化的目标;从级为粒子群算法,在主级工艺路径约束下的设备排产中实现工件流通时间最小化的目标。然后,以设备负荷和工序加工时间为启发式信息设计蚂蚁在工序可用设备间转移概率;基于粒子向量优先权值的大小关系设计解码方法实现设备上的工序排产。最后,通过仿真和比较实验,验证了该算法的有效性。     

An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

This paper investigates a waste collection problem with the consideration of midway disposal pattern. An artificial bee colony (ABC)-based hybrid approach is developed to handle this problem, in which the hybrid ABC algorithm is proposed to generate the better optimum-seeking performance while a heuristic procedure is proposed to select the disposal trip dynamically and calculate the carbon emissions in waste collection process. The effectiveness of the proposed approach is validated by numerical experiments. Experimental results show that the proposed hybrid approach can solve the investigated problem effectively. The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four popular metaheuristics, namely a genetic algorithm, a particle swarm optimization algorithm, an enhanced ABC algorithm and a hybrid particle swarm optimization algorithm. It is also found that the midway disposal pattern should be used in practice because it reduces the carbon emission at most 7.16% for the investigated instances.     

基于遗传粒子群混合算法的供应链调度优化

针对大规模定制模式下的供应链调度优化问题,首先将供应链调度优化问题进行数学规划建模,并通过一些定义将模型映射为有向图,以便于智能搜索算法的应用;然后,结合遗传算法全局搜索能力强与粒子群算法收敛速度快的特点,进行优势互补,构造了一种混合算法;最后,运用混合算法对供应链优化调度问题模型进行求解.通过与其他算法进行比较,所得结果表明混合算法有着更好的整体性能.     

异构系统中基于可用性的粒子群任务调度算法

任务调度技术是并行分布式系统中的关键技术之一,对系统的性能起着重要作用,但通常情况下大型系统的任务调度问题属于NP问题。而现代启发式生物进化算法是找出很多NP问题近似解的有效方法。本文将粒子群算法应用于基于可用性的网格系统调度中,提出了一种调度算法,对算法的性能进行了理论分析和模拟实验。结果表明:和最近文献中的基于可用性的调度算法SSAC相比,所提出的新算法在保证系统资源具有同样的可用性条件下,能够产生更好的调度长度。     

基于值序的二元约束满足问题粒子群算法

提出了一个基于最小冲突启发式值序的二元约束满足问题粒子群算法，利用值序对值的选取方式代替随机选择的盲目搜索方式，使群体在探索解空间的时候，选择有希望能找到全局解的地方搜索。使用随机约束满足问题的实验表明，改进后的算法比原算法能以更快的速度收敛到全局解，无论在迭代次数还是运行时间上均能数倍提高算法的效率。     

求解车间调度问题的2阶段混合粒子群优化算法

针对车间调度问题,提出了一种2阶段混合粒了群算法(TS-HPSO).该算法在第1阶段为每个粒子设置较大的惯性系数w,同时去掉了粒子的社会学习能力,从而保证每个微粒在局部范围内充分搜索.第2阶段的混合粒子群算法以第1阶段每个粒子找到的最好解作为初始解,同时以遗传算法中的变异操作保证粒了多样性;为保证算法的寻优能力,对全局gbest进行贪婪邻域搜索.计算结果证明了本算法的有效性.     

基于PSODE的网格任务调度算法

针对网格计算中任务在各个资源之间的调度问题,提出了一种网格环境下PSODE的任务调度算法.该算法实现了计算资源、存储资源、带宽资源、数据资源的利用率最高化和代价最低化.对基本粒子群算法和差分进化算法进行了分析,通过构造算法函数、适应值函数和权重公式,建立了粒子群差分混合算法并对其进行优化,介绍了算法的实现过程.实验结果表明,该算法与其它调度算法比较,具有良好的性能.     

一种求解车间调度的混合算法

针对流水车间作业调度问题, 提出了一种基于``alldifferent'约束的混合进化算法(Hybrid particle and genetic algorithm, HPGA), 将粒子群算法、遗传操作及模拟退火策略有效地结合在一起. 为了提高算法的求解质量, 引入了一种随机邻域搜索策略. 最后将此算法在不同规模的实例上进行了测试, 并与其他几种最近提出的具有代表性的算法进行了比较. 结果表明, 无论是在求解质量还是收敛速度方面都优于其他几种算法.     

基于混合粒子群算法的云计算任务调度研究

任务调度是云计算系统可靠运行的关键,云计算环境中要处理的任务量巨大,考虑到云计算任务调度和QoS的优化问题,提出一种混合粒子群优化算法用于云任务调度。算法中引入遗传算法的交叉和变异思想,并结合随迭代次数变化的变异指数,保证种群进化初期具有较高的全局搜索能力,避免出现"早熟",同时将爬山算法引入粒子群算法,改善局部搜索能力。实验结果显示该算法具有很好的寻优能力,是一种有效的云计算任务调度算法。     

A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

In this paper, a novel hybrid discrete particle swarm optimization algorithm is proposed to solve the dual-resource constrained job shop scheduling problem with resource flexibility. Particles are represented based on a three-dimension chromosome coding scheme of operation sequence and resources allocation. Firstly, a mixed population initialization method is used for the particles. Then a discrete particle swarm optimization is designed as the global search process by taking the dual-resources feature into account. Moreover, an improved simulated annealing with variable neighborhoods structure is introduced to improve the local searching ability for the proposed algorithm. Finally, experimental results are given to show the effectiveness of the proposed algorithm.     

利用改进遗传算法优化PID参数

为了改善单纯遗传算法早熟收敛与寻优能力不足的问题,将粒子群算法引入遗传算法变异操作中,提出了一种基于遗传算法与粒子群算法的组合算法。将改进的遗传算法应用于PID控制器参数优化中,通过仿真实验表明,新算法效果明显优于单纯遗传算法,能有效克服早熟收敛现象、降低随机性初始种群的影响、提高算法收敛精度,具有良好的收敛性和寻优能力。     

Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

An auto controlled ant colony optimization algorithm controls the behavior of the ant colony algorithm automatically based on a priori heuristic. During the experimental study of auto controlled ACO algorithm on grid scheduling problem, it was observed that the induction of lazy ants not only reduces the time complexity of the algorithm but also produces better results on the given objectives. Lazy ants are basically a mutated version of active ants that remain alive till the fitter lazy ants are generated in the successive generations. This work presents an improved auto controlled ACO algorithm using the lazy ant concept. Performance study reveals the efficacy and the efficiency achieved by the proposed algorithm. A comparative study of the proposed method with some other recent meta-heuristics such as auto controlled ant colony optimization algorithm, genetic algorithm, quantum genetic algorithm, simulated annealing and particle swarm optimization for grid scheduling problem exhibits so.