A multi-objective particle swarm optimization for project selection problem

Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances.     

基于离散微粒群算法的动态Web服务选择

Web服务作为一种新型的Web应用模式近年来得到了迅速的发展.如何高效动态地把现存的各种Web服务整合起来以形成新的满足不同用户需求的增值的复杂服务,已成为新的应用需求和研究热点.针对服务选择问题,设计了一种面向动态Web服务选择的离散微粒群算法,并结合服务选择研究背景,提出了3种速度计算算子和一种位置进化方程.针对进化算法容易陷入局部极值这一共同缺陷,定义了微粒无希望/重希望准则,以保证微粒群的多样性,增强全局搜索能力.理论分析和实验结果表明,该算法不仅具有较快的收敛速度,而且具有较好的全局收敛性能;同时说明Max运算在服务选择中具有较好的综合性能.     

基于NPSO算法求解车间作业调度问题

研究车间生产生产调试系统,使资源达到优化配置,实现了一种基于小生境的粒子群优化算法用于求解车间作业调度问题。通过在粒子群算法中引入共享函数和共享适应度函数分别用来计算粒子间的共享度和粒子的共享适应值,并用粒子的共享适应值来反映其适应能力。粒子的位置越相近,则粒子间的共享度越大,相应粒子的共享适应值则越小。通过设置小生境半径的方式,将整个粒子群分解为多个小生境子种群,并通过设置小生境中的最大粒子个数参数,严格控制各个小生境中的粒子数量,使得所有粒子尽可能地分布到整个搜索空间的不同局部峰值区域,从而有效求得问题的全局最优值。仿真结果表明了算法对经典JSP问题求解的优良性能。     

A negotiation‐based service selection approach using swarm intelligence and kernel density estimation

Nowadays, the cloud computing environment is becoming a natural choice to deploy and provide Web services that meet user needs. However, many services provide the same functionality and high quality of service (QoS) but different self‐adaptive behaviors. In this case, providers' adaptation policies are useful to select services with high QoS and high quality of adaptation (QoA). Existing approaches do not take into account providers' adaptation policies in order to select services with high reputation and high reaction to changes, which is important for the composition of self‐adaptive Web services. In order to actively participate to compositions, candidate services must negotiate their self‐* capabilities. Moreover, they must evaluate the participation constraints against their capabilities specified in terms of QoS and adaptation policies. This paper exploits a variant of particle swarm optimization and kernel density estimation in the selection of service compositions and the concurrent negotiations of their QoS and QoA capabilities. Selection and negotiation processes are held between intelligent agents, which adopt swarm intelligence techniques for achieving optimal selection and optimal agreement on providers' offers. To resolve unknown autonomic behavior of candidate services, we deal with the lack of such information by predicting the real QoA capabilities of a service through the kernel density estimation technique. Experiments show that our solution is efficient in comparison with several state‐of‐the‐art selection approaches.     

基于QoS的组合服务优化选择问题建模与求解

提出了一种支持Web服务动态组合的框架,并在此基础上对Web服务组合中的服务优化选择问题建模,根据用户设置的QoS约束条件,将服务优化选择问题分成3类,并分别给出了相应的服务选择算法。实验证明算法在保证一定优度的同时亦具有良好的时间性能。     

A multicriteria optimization model for cloud service provider selection in multicloud environments

Multicloud computing is a strategy that helps customers to reduce reliance on any single cloud provider (known as the vendor lock-in problem). The value of such strategy increases with proper selection of qualified service providers. In this paper, a constrained multicriteria multicloud provider selection mathematical model is proposed. Three metaheuristics algorithms (simulated annealing [SA], genetic algorithm [GA], and particle swarm optimization algorithm [PSO]) were implemented to solve the model, and their performance was studied and compared using a hypothetical case study. For the sake of comparison, Taguchi's robust design method was used to select the algorithms' parameters values, an initial feasible solution was generated using analytic hierarchy process (AHP)—as the most used method to solve the cloud provider selection problem in the literature, all three algorithms used that solution and, in order to avoid AHP limitations, another initial solution was generated randomly and used by the three algorithm in a second set of performance experiments. Results showed that SA, GA, PSO improved the AHP solution by 53.75%, 60.41%, and 60.02%, respectively, SA and PSO are robust because of reaching the same best solution in spite of the initial solution.     

Per-flow optimal service selection for Web services based processes

With the development of the Service-Oriented Computing (SOC) paradigm, flexible business processes can be defined from independently developed services. Multiple services corresponding to the same functionality but characterized by different Quality of Service (QoS) attributes can be offered by different service providers and the best set of Web services can be selected at run-time in order to maximize the QoS for end users.In the literature many approaches have been proposed for the optimal service selection which is usually performed on a per-request basis, i.e., considering a single process invocation. In this paper we propose a broker-based framework which solves the optimal service selection on a per-flow basis. Multiple applications, defined as different BPEL processes are considered at the same time and multiple requests to the same process are optimized concurrently.Service selection is formulated as a constrained non-linear multi-criteria optimization problem and an heuristic algorithm is proposed to determine a scalable and efficient solution.A comparison with top performing state-of-the-art approaches for a number of different scenarios of interest is also provided. Results show that the overall optimization time reduction which can be achieved by our solution is proportional to the system incoming workload. Order of magnitudes optimization time improvements can be obtained if compared to alternative methods. Moreover, our solution is robust to workload prediction uncertainty.     

复形法粒子群优化算法研究

针对基本粒子群优化算法对复杂函数优化时难以获得最优解的缺陷,提出了一种复形粒子群优化算法。该算法采用复形法来提高粒子的局部搜索能力,从而保证了算法能够跳出局部最优,获得全局最优解。实验结果表明,与文献算法相比,该算法在基准函数优化时具有更强的寻优能力和更高的搜索精度。     

Web服务个性化推荐研究综述

随着Web服务的广泛使用和互联网上服务数量的增加,如何向用户提供最佳的服务选择列表成为了新的挑战.Web服务个性化推荐实现了由被动接受用户请求向主动感知用户需求的转变.个性化的Web服务推荐方法已经成为Web服务发现和选择的有效辅助手段.Web服务的个性化推荐技术也成为了近年来服务计算领域的研究热点.对当前Web服务个性化推荐的文献进行了归类分析,总结了当前Web服务个性化推荐的技术现状、研究方法和实验的数据集,列出了未来Web服务个性化推荐研究热点和挑战.     

Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition

Web service composition combines available services to provide new functionality. The various available services have different quality-of-service (QoS) attributes. Building a QoS-optimal web service composition is a multi-criteria NP-hard problem. Most of the existing approaches reduce this problem to a single-criterion problem by aggregating different criteria into a unique global score (scalarization). However, scalarization has some significant drawbacks: the end user is supposed to have a complete a priori knowledge of its preferences/constraints about the desired solutions and there is no guarantee that the aggregated results match it. Moreover, non-convex parts of the Pareto set cannot be reached by optimizing a convex weighted sum. An alternative is to use Pareto-based approaches that enable a more accurate selection of the end-user solution. However, so far, only few solutions based on these approaches have been proposed and there exists no comparative study published to date. This motivated us to perform an analysis of several state-of-the-art multi-objective evolutionary algorithms. Multiple scenarios with different complexities are considered. Performance metrics are used to compare several evolutionary algorithms. Results indicate that GDE3 algorithm yields the best performances on this problem, also with the lowest time complexity.     

异构多核协作系统的混沌离散粒子群NoC映射算法

针对异构多核片上网络(NoO的任务映射问题,根据IP核的选择以及IP核向NoC平台中位置映射的两个阶段的不同特点,分别提出能耗和延时的粗略估算模型和精确计算模型。为避免离散空间搜索解落入局部最优,设计了混沌扰动机制。提出了带混沌扰动机制的改进型离散粒子群优化算法,以搜索能耗和延时优化的多目标NoC映射方案,该算法比传统优化算法在能耗和延时上有显著的性能提高。     

物流服务的BPEL流程建模及组合算法研究

我国的物流企业呈现了规模小,市场份额小,各自为战,功能单一的现象。各公司优势不能结合起来,不符合现代物流的要求。针对以上问题,基于bpel工作流技术,将每个物流节点选择的不同公司提供的服务组合成一个业务流程。用改进的粒子群算法解决了两个问题：如何在各个节点的服务候选集中快速选取一套符合要求的最优服务;在不能同时服务多个用户的情况下,按怎样顺序服务用户。实现了服务的合理组合,提高整体的物流服务水平。     

一种求解高维复杂函数优化问题的混合粒子群优化算法

对于高维复杂函数,传统的确定性算法易陷入局部最小,而单一的全局随机搜索算法收敛速度慢.本文通过将粒子群优化算法和单纯形法相结合,利用前者搜索速度快、搜索范围广和后者收敛速度快的特 性,提出一种快速、易实现的混合粒子群优化算法.基于典型高维复杂函数的仿真表明:该混合算法效率高、优化性能好、对初值具有很强的鲁棒性,其性能大大优于单一的优化方法.     

含区间参数多目标系统的微粒群优化算法

参数不确定优化问题是实践中经常遇到的复杂优化问题, 现有方法多针对单目标函数的情况. 本文利用微粒群优化算法解决含区间参数多目标优化问题, 提出一种基于概率支配的多目标微粒群优化算法. 该算法通过定义概率支配关系, 比较所得解的优劣; 基于 σ 区间值, 选择微粒的全局极值点, 并给出新的微粒个体极值点及外部储备集的更新策略. 与传统多目标微粒群优化算法比较, 仿真结果表明本文所提算法的有效性.     

GA模糊控制支持QOS约束的Web服务选择模型

Web服务是一种基于分布式组件的软件平台,是面向服务的Internet应用,如何动态地选择、绑定并调用最适合用户需求的Web服务,是一个成熟的Web服务构架亟待解决的问题之一。提出了一种用遗传算法结合模糊控制系统,实现动态、精确、具有自学习能力的个性化Web服务选择的策略,并在此基础上设计了一种基于模糊控制专家系统支持QOS的Web服务模型框架。     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

A framework for QoS-aware binding and re-binding of composite web services

QoS-aware dynamic binding of composite services provides the capability of binding each service invocation in a composition to a service chosen among a set of functionally equivalent ones to achieve a QoS goal, for example minimizing the response time while limiting the price under a maximum value.This paper proposes a QoS-aware binding approach based on Genetic Algorithms. The approach includes a feature for early run-time re-binding whenever the actual QoS deviates from initial estimates, or when a service is not available. The approach has been implemented in a framework and empirically assessed through two different service compositions.     

基于社团服务链的Web服务组合方法

针对Web服务组合的时间效率提高问题,提出了一种基于服务社团和服务链的Web服务组合方法。在构造的服务网络上应用基于信息中心度的服务社团发现方法,将Web服务网络划分为不同的服务社团,然后构造了社团服务链发现算法和基于服务链的Web服务组合算法,这些算法将服务社团内Web服务之间的所有可组合关联转变成服务链,实现了基于社团服务链和服务质量(QoS)剪枝的Web服务组合过程。实验结果表明,与传统的图深度遍历Web服务组合方法相比,基于社团服务链的Web服务组合方法在5个测试集上的响应时间平均提高了46%,最好情况为67%。社团服务链可以有效地减少针对当前服务请求的服务搜索空间,提高服务组合的时间效率。     

基于粒子群优化的粒子滤波定位方法

为了实现移动机器人精确高效的自定位,提出了基于粒子群优化的粒子滤波定位方法.文章分析了常规粒子滤波定位方法存在的不足之处.将最新观测值融合到采样过程中,并利用粒子群优化算法提高了常规粒子滤波器的预估性能.接下来,建立了系统的概率运动模型和感知模型,并利用粒子群优化粒子滤波方法解决了移动机器人的自定位问题.粒子群优化算法的优化结果使得采样集向后验概率密度分布取值较大的区域运动,从而克服了粒子贫乏问题并且显著地降低了精确定位所需的粒子数.仿真实验表明该算法的有效性.     

A bilevel integer programming method for blended composite structures

This paper proposes a new approach for the design of a composite structure. This approach is formulated as an optimization problem where the weight of the structure is minimized such that a reserve factor is higher than a threshold. The thickness of each region of the structure is optimized together with its stacking sequence and the ply drop-offs. The novelty of this approach is that, unlike in common practice, the optimization problem is not simplified and split into two steps, one for finding the thicknesses and one for the stacking sequence. The optimization problem is solved without any simplification assumption. It is formulated as a bilevel integer programming and it uses the backtracking procedure to satisfy the blending and the manufacturing rules. Some numerical experiments are performed to show the efficiency of the proposed optimization method over complex cases which cannot be solved with the existing methods.