基于离散粒子群优化的云工作流调度

针对云工作流调度问题面临的安全威胁,首先采用云模型量化任务与虚拟机资源的安全性,通过安全云相似度衡量用户对任务所分配虚拟机资源的安全满意程度;然后建立考虑安全性、完成时间和使用费用的云工作流调度模型,并提出基于离散粒子群优化的云工作流调度算法;最后对所提算法进行仿真实验.实验结果表明,与同类算法相比,该算法在安全效用值、完成时间、使用费用和负载均衡离差方面具有较好的性能表现.     

基于微粒群算法的网格工作流优化调度问题的研究

网格工作流中的调度问题是一个复杂且具有挑战性的问题,它影响着网格工作流执行成功与否及效率的高低.针对具有时序和因果约束关系的网格工作流优化调度问题进行了研究,建立了网格工作流的任务调度模型和调度问题的目标模型,并应用微粒群算法来优化网格工作流中任务的调度.实验结果证明该算法优于传统的调度算法.     

混合自适应粒子群工作流调度优化算法

针对具有截止期的云工作流完成时间与执行成本冲突的问题,提出一种混合自适应粒子群工作流调度优化算法（HAPSO）。首先,基于截止期建立有向无环图（DAG）云工作流调度模型;然后,通过范数理想点与自适应权重的结合,将DAG调度模型转化为权衡DAG完成时间和执行成本的多目标优化问题;最后,在粒子群优化（PSO）算法的基础上引入自适应惯性权重、自适应学习因子、花朵授粉算法的概率切换机制、萤火虫算法（FA）和粒子越界处理方法,从而平衡粒子群的全局搜索与局部搜索能力,进而求解DAG完成时间与执行成本的目标优化问题。实验中对比分析了PSO、惯性权重粒子群算法（WPSO）、蚁群算法（ACO）和HAPSO的优化结果。实验结果表明,HAPSO在权衡工作流（30～300任务数）完成时间与执行成本的多目标函数值上降低了40.9%～81.1%,HAPSO在工作流截止期约束下有效权衡了完成时间与执行成本。此外,HAPSO在减少完成时间或降低执行成本的单目标上也有较好的效果,验证了HAPSO的普适性。     

多目标FJSP的一维编码粒子群优化求解方法

以单件小批量生产方式为主的柔性车间调度中,快速得到满足低生产成本、高生产效率,避免瓶颈发生的调度方案,是调度优化算法的设计目标。就此建立了以制造期、机床总负荷和单机最大负荷为综合目标的柔性车间调度问题（Flexible Job-shop Scheduling Problems,FJSP）优化模型;设计了一种以概率值为分量的一维粒子群优化算法,通过概率区间划分将连续粒子分量离散化,结合完工时间最早启发式规则,实现工序的排序与加工机床的选取。通过不同规模算例的比较,分析结果表明该方法在求解较大规模问题时具有一定的优势。     

多目标微粒群优化算法

通过设计一种Pareto解集过滤器,并在此基础上给出多目标优化条件下的微粒群算法群体停滞判断准则,基于该准则提出了一种多目标微粒群优化算法。算法利用Pareto解集过滤器提高了候选解的多样性,并使用图形法将所提算法与经典的多目标优化进化算法在一组标准测试函数上进行了比较,结果表明算法具有更好的搜索效率。     

Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining

Neural Computing and Applications - The most challenging issues in association rule mining are dealing with numerical attributes and accommodating several criteria to discover optimal rules without...     

基于多策略的多目标粒子群优化算法

针对目前多目标粒子群优化算法的收敛性能和非劣解的多样性不能同时得到满足等缺陷,提出一种基于多策略的多目标粒子群优化算法（Multi-Objective Particle Swarm Optimization algorithm for Multi-Strategy,MS-MOPSO）。采用非支配排序和拥挤距离排序相结合策略,重新划分外部种群和进化种群;采用小生境选择策略,在外部种群中选择最佳粒子作为领导粒子,用于领导进化种群中粒子的进化;在进化种群中利用多尺度高斯变异策略,平衡算法的全局搜索和局部精确搜索;采用邻域认知个体极值更新策略,不断更新个体极值。将该算法应用到典型的多目标测试函数,并与其他多目标优化算法进行对比分析,测试结果表明该算法中四个策略的有效性和互补性,同时验证了该算法不但具有较好的收敛性和收敛速度,而且该算法最优解的分布具有良好的均匀性和多样性。     

Multi-objective reliability-redundancy allocation problem using particle swarm optimization

This paper considers the multi-objective reliability redundancy allocation problem of a series system where the reliability of the system and the corresponding designing cost are considered as two different objectives. Due to non-stochastic uncertain and conflicting factors it is difficult to reduce the cost of the system and improve the reliability of the system simultaneously. In such situations, the decision making is difficult, and the presence of multi-objectives gives rise to multi-objective optimization problem (MOOP), which leads to Pareto optimal solutions instead of a single optimal solution. However in order to make the model more flexible and adaptable to human decision process, the optimization model can be expressed as fuzzy nonlinear programming problems with fuzzy numbers. Thus in a fuzzy environment, a fuzzy multi-objective optimization problem (FMOOP) is formulated from the original crisp optimization problem. In order to solve the resultant problem, a crisp optimization problem is reformulated from FMOOP by taking into account the preference of decision maker regarding cost and reliability goals and then particle swarm optimization is applied to solve the resulting fuzzified MOOP under a number of constraints. The approach has been demonstrated through the case study of a pharmaceutical plant situated in the northern part of India.     

基于聚集密度的粒子群多目标优化算法

为了改善粒子群多目标优化算法的分布性,引入了聚集密度以进行精英集的更新。其基本思想为：计算群体中每个个体的聚集密度,根据目标函数值和聚集密度定义一个偏序集,采用比例选择原则依次从偏序集中选择个体,更新精英集。通过数值实验用量化指标研究了新算法的收敛性和分布性,结果表明：新算法的收敛性与常规粒子群多目标优化算法相当,但分布性有了明显的提高。     

一种求解典型JSP的改进离散粒子群优化算法

针对NP-hard性质的作业车间调度问题, 设计了一种改进的离散粒子群优化算法。引入遗传算法交叉算子和变异算子来实现粒子的更新, 并将变异思想和模拟退火算法思想融入该算法中对全局最优粒子的邻域进行局部搜索, 很好地防止了算法出现早熟收敛。通过将该算法和标准粒子群优化算法用于求解典型JSP, 计算结果对比表明, 改进的算法具有很强的全局寻优能力; 就综合解的质量和计算效率而言, 改进算法优于标准粒子群优化算法。同时, 将该算法结果与文献中其他相关算法结果进行比较, 验证了该改进算法的有效性。该算法能够有效地、高质量地解决作业车间调度问题。     

A discrete particle swarm optimization algorithm for scheduling parallel machines

As a novel evolutionary technique, particle swarm optimization (PSO) has received increasing attention and wide applications in a variety of fields. To our knowledge this paper investigates the first application of PSO algorithm to tackle the parallel machines scheduling problem. Proposing equations analogous to those of the classical PSO equations, we present a discrete PSO algorithm (DPSO) to minimize makespan (C_max) criterion. We also investigate the effectiveness of DPSO algorithm through hybridizing it with an efficient local search heuristic. To verify the performance of DPSO algorithm and its hybridized version (HDPSO), comparisons are made through using a recently proposed simulated annealing algorithm for the problem, addressed in the literature, as a comparator algorithm. Computational results signify that the proposed DPSO algorithm is very competitive and can be rapidly guided when hybridizing with a local search heuristic.     

多资源约束的项目调度问题离散粒子群算法

提出了一种求解多资源约束项目调度问题的离散粒子群算法。调度方案采用了优先权整数编码方法,使用稀疏存储邻接矩阵判定先后约束,使用活动单位时间推迟策略来满足资源约束。算法的创新点在于直接使用了整数编码的调度方案参与迭代计算,并依据计算结果提供的信息做出位置交换。不但解决了处理离散整数编码粒子的难题,而且容易实现,亦可推广至类似整数编码优化问题。测试用例计算结果表明了算法的可行性和有效性。     

A discrete version of particle swarm optimization for flowshop scheduling problems

Particle swarm optimization (PSO) is a novel metaheuristic inspired by the flocking behavior of birds. The applications of PSO to scheduling problems are extremely few. In this paper, we present a PSO algorithm, extended from discrete PSO, for flowshop scheduling. In the proposed algorithm, the particle and the velocity are redefined, and an efficient approach is developed to move a particle to the new sequence. To verify the proposed PSO algorithm, comparisons with a continuous PSO algorithm and two genetic algorithms are made. Computational results show that the proposed PSO algorithm is very competitive. Furthermore, we incorporate a local search scheme into the proposed algorithm, called PSO-LS. Computational results show that the local search can be really guided by PSO in our approach. Also, PSO-LS performs well in flowshop scheduling with total flow time criterion, but it requires more computation times.     

基于交叉和变异的多目标粒子群算法

为了保证粒子群算法求得的非劣解尽可能接近真实的Pareto前沿并保持多样性分布. 提出一种基于交叉和变异的多目标粒子群算法(CMMOPSO). 在CMMOPSO算法中, 首先, 识别Pareto前沿的稀疏部分包含的粒子, 并对这些粒子进行交叉操作以增加多样性分布; 其次, 对于远离Pareto前沿的粒子进行变异操作, 以提升粒子向真实的Pareto前沿飞行的概率. 在基准函数的测试中, 结果显示CMMOPSO算法比其它算法有更好的运行效果. 因此, CMMOPSO算法可以作为求解多目标问题的一种有效算法.     

Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization

In this paper, a fuzzy multi-objective programming problem is considered where functional relationships between decision variables and objective functions are not completely known to us. Due to uncertainty in real decision situations sometimes it is difficult to find the exact functional relationship between objectives and decision variables. It is assumed that information source from where some knowledge may be obtained about the objective functions consists of a block of fuzzy if-then rules. In such situations, the decision making is difficult and the presence of multiple objectives gives rise to multi-objective optimization problem under fuzzy rule constraints. In order to tackle the problem, appropriate fuzzy reasoning schemes are used to determine crisp functional relationship between the objective functions and the decision variables. Thus a multi-objective optimization problem is formulated from the original fuzzy rule-based multi-objective optimization model. In order to solve the resultant problem, a deterministic single-objective non-linear optimization problem is reformulated with the help of fuzzy optimization technique. Finally, PSO (Particle Swarm Optimization) algorithm is employed to solve the resultant single-objective non-linear optimization model and the computation procedure is illustrated by means of numerical examples.     

云计算环境下基于粒子群算法的多目标优化

为了优化资源的部署调度,需要考虑处理费用、传输费用,并提高云计算的性能.对云计算环境下特点进行了研究,把云计算环境下的数据部署和任务调度问题映射为处理交互图,对处理交互图进行分析、提出了多目标优化模型,并通过粒子群算法对多目标模型进行优化.仿真结果表明,该多目标优化模型和算法不但能优化处理时间、传输时间,也能优化处理费用和传输费用.     

基于离散微粒群算法求解背包问题研究

微粒群算法(PSO)是一种新的演化算法,主要用于求解数值优化问题.基于离散微粒群算法(DPSO)分别与处理约束问题的罚函数法和贪心变换方法相结合,提出了求解背包问题的两个算法:基于罚函数策略的离散微粒群算法(PFDPSO)和基于贪心变换策略的离散微粒群算法(GDPSO).通过将这两个算法与文献[7]中的混合微粒群算法(Hybrid_PSO)进行数值计算比较发现:对于求解大规模的背包问题,GDPSO非常优秀,其求解能力优于Hybrid_PSO和PFDPSO,是求解背包问题的一种非常有效的方法.     

自适应种群更新策略的多目标粒子群算法

针对粒子种群较差的局部搜索能力,提出了一种自适应种群更新策略的多目标粒子群算法。该算法在每次种群进行迭代时,根据种群的多样性测度以及每个粒子的适应度值,自适应地改变速度权重,以此来提高种群粒子在局部搜索时的活性,使算法具有较强的局部搜索能力同时又保留了足够的全局搜索能力。最后利用多组经典测试样例进行仿真,并与传统的粒子群算法以及速度线性衰减算法做比较,在单目标优化中,自适应粒子群算法能够更快地寻找最优位置;在多目标优化中,自适应粒子群算法能够更快速地收敛于帕累托最优边界。     

基于决策者偏好区域的多目标粒子群算法研究*

多目标优化问题中,决策者往往只对目标空间的某一区域感兴趣,因此需要在这一特定的区域能够得到比较稠密的Pareto解,但传统的方法却找出全部的Pareto前沿,决策效率不高。针对该问题,给出了基于决策者偏好区域的多目标粒子群优化算法。它只求出与决策者偏好区域相关的部分Pareto最优集,从而减少了进化代数,加快收敛速度,有利于决策者进行更有效的决策。算法把解与偏好区域的距离作为影响引导者选择和剪枝策略的一个因素,运用格栅方法实现解在Pareto边界分布的均匀性。仿真结果表明该算法是有效的。     

基于树形结构无界存档的多目标粒子群算法

多目标优化算法大多采用基于线性链表结构的有界Pareto存档策略,其存在迭代过程中Pareto前沿震荡衰退等弊端以及相关参数难以预先确定等技术难题.为此,构造一种适用于大规模存档集合的树形结构,并利用其取代线性结构以保证存档维护与管理的高效性,进而提出基于树形结构的无界存档策略.在此基础上,将基于正交设计的种群初始化、基于树形结构的存档更新以及基于树形结构的最优个体选择引入多目标粒子群优化,提出基于树形结构无界存档的多目标粒子群算法.最后,通过测试函数上的仿真实验验证了所提出策略与算法的科学性和有效性.