Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network(ANN) based software reliability model trained by novel particle swarm optimization(PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.

Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.

Reliability analysis of earth slopes using hybrid chaotic particle swarm optimization

A numerical procedure for reliability analysis of earth slope based on advanced first-order second-moment method is presented,while soil properties and pore water pressure may be considered as random variables.The factor of safety and performance function is formulated utilizing a new approach of the Morgenstern and Price method.To evaluate the minimum reliability index defined by Hasofer and Lind and corresponding critical probabilistic slip surface,a hybrid algorithm combining chaotic particle swarm optimization and harmony search algorithm called CPSOHS is presented.The comparison of the results of the presented method,standard particle swarm optimization,and selected other methods employed in previous studies demonstrates the superior successful functioning of the new method by evaluating lower values of reliability index and factor of safety.Moreover,the presented procedure is applied for sensitivity analysis and the obtained results show the influence of soil strength parameters and probability distribution types of random variables on the reliability index of slopes.

Hybrid particle swarm optimization for multiobjective resource allocation

Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

Active Power Allocation of Virtual Synchronous Generator Using Particle Swarm Optimization Approach

In recent years, the penetration of renewable energy sources (RES) is increasing due to energy and environmental issues, causing several problems in the power system. These problems are usually more apparent in microgrids. One of the problems that could arise is frequency stability issue due to lack of inertia in microgrids. Lack of inertia in such system can lead to system instability when a large disturbance occurs in the system. To solve this issue, providing inertia support to the microgrids by a virtual synchronous generator (VSG) utilizing energy storage system is a promising method. In applying VSG, one important aspect is regarding the set value of the active power output from the VSG. The amount of allocated active power during normal operation should be determined carefully so that the frequency of microgrids could be restored to the allowable limits, as close as possible to the nominal value. In this paper, active power allocation of VSG using particle swarm optimization (PSO) is presented. The results show that by using VSG supported by active power allocation determined by the method, frequency stability and dynamic stability of the system could be improved.

Resource allocation optimization of equipment development task based on MOPSO algorithm

Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.

Reliability Estimation for Component-Based Software Using General Masking Grouped Data

Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering.Furthermore,a general maskedbased additive non-homogeneous Poisson process(NHPP) model was considered to analyze component reliability.However,the problem of masked-based additive model lies in the difficulty of estimating parameters.The maximum likelihood estimation procedure was derived to estimate parameters.Finally,a numerical example was given to illustrate the applicability of proposed model,and the immune particle swarm optimization(IPSO) algorithm was used in maximize log-likelihood function.

Thermo-mechanical fatigue reliability optimization of PBGA solder joints based on ANN-PSO

Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The simulation experiments of accelerated thermal cycling test were performed by ANSYS software. Based on orthogonal array experiments, a back-propagation artificial neural network (BPNN) was used to establish the nonlinear multivariate relationship between thermo-mechanical fatigue reliability and control factors. Then, PSO was applied to obtaining the optimal levels of control factors by using the output of BPNN as the affinity measure. The results show that the control factors, such as print circuit board (PCB) size, PCB thickness, substrate size, substrate thickness, PCB coefficient of thermal expansion (CTE), substrate CTE, silicon die CTE, and solder joint CTE, have a great influence on thermo-mechanical fatigue reliability of PBGA solder joints. The ratio of signal to noise of ANN-PSO method is 51.77 dB and its error is 33.3% less than that of Taguchi method. Moreover, the running time of ANN-PSO method is only 2% of that of the BPNN. These conclusions are verified by the confirmative experiments.

Enhanced PSO Based Energy-Efficient Resource Allocation and CQI Based MCS Selection in LTE-A Heterogeneous System

In order to maximize system energy efficiency(EE) under user quality of service(Qo S) restraints in Long Term Evolution-Advanced(LTE-A) networks,a constrained joint resource optimization allocation scheme is presented,which is NP-hard. Hence,we divide it into three sub-problems to reduce computation complexity,i.e.,the resource block(RB) allocation,the power distribution,and the modulation and coding scheme(MCS) assignment for user codewords. Then an enhanced heuristic approach GAPSO is proposed and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover,a novel MCS allocation scheme is put forward,which could make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching,and that the joint resource allocation scheme could improve energy efficiency effectively under user Qo S requirements.

Resource Allocation Algorithm Based on PSO-GA for Multi-User OFDM System

In order to minimize the transmitted power in the multi-user orthogonal frequency division multiplexing（OFDM） system, a scheme combining the improved particle swarm optimization（POS） algorithm with genetic algorithm（GA） is proposed to optimize the sub-carriers and bits allocation. In the algorithm, a random velocity between the maximum and minimum particle velocity is used as the updating velocity instead of maximum or minimum velocity when the updated particle velocity is higher than the maximum particle velocity or lower than the minimum particle velocity. Then, the convergence population is used as the initial population of the genetic algorithm to optimize the subcarriers and bits allocation further. Simulation results show that the transmitted power of the proposed algorithm is about 2 d B to 10 d B lower than that of the genetic algorithm, particle swarm optimization algorithm, and Zhang＇s algorithm.

Thrust Allocation Optimization in Dynamic Positioning Vessels with Main PropellerRudders

In order to deal with the chattering of rudder angle and the problem of non-convex attainable thrust regions,introduce the concept of dynamic attainable region for each thruster and rudder to limit the thruster rotational speed and the rudder angle,and decompose the thrust allocation optimization problem into several optimization sub-problems.The optimization sub-problems were solved by particle swarm optimization(PSO) algorithm.Simulation studies with comparisons on a model ship were carried out to illustrate the effectiveness of the proposed thrust allocation optimization method.

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

生存进化阶段性搜索微粒群算法及其可靠性冗余分配优化应用

为高效解决含有异质冗余的多态系统(MSS)可靠性优化问题,并弥补微粒群优化(PSO)算法易早熟收敛的不足,从作用力方式和种群拓扑结构两方面对算法进行改进。改进PSO算法中单一的作用力方式,设置前后两个搜索阶段,对应两个搜索阶段分别构造平衡引斥力方式和双层引力(个体和全局最优解引力、中间适应度微粒引力)方式,提出阶段性搜索微粒群(SPSO)算法;利用生物个体“择友而交”和优胜劣汰的生存体系构建生存进化(SE)拓扑结构,以结构演化和算法进化并行方式将该拓扑结构融入SPSO算法,提出生存进化阶段性搜索微粒群(SPSO-SE)算法,进一步提升算法的优化性能;利用Benchmark函数对所提算法与PSO的改进算法进行测试对比,结果表明,所提SPSO-SE算法具有更好的寻优能力。采用SPSO-SE算法对串-并联和桥式结构的多态系统的可靠性冗余分配问题进行优化,得到的系统结构费用更低、可靠度更高。

基于协同进化粒子群优化算法的水资源配置模型及应用

面向新发展阶段的城市水资源配置具有多目标、多变量、约束条件复杂、求解结果非线性、求解过程困难等特征。针对线性规划、动态规划、非线性规划等传统优化算法在解决水资源配置问题中求解结果不合理、计算效率低,求解多目标问题收敛慢等问题,提出了基于协同进化粒子群优化(CPSO)算法的多目标水资源优化配置模型。以郑州市为例,构建了以实现社会、经济和生态效益的最大化为目标,供水量、需水量、供水能力和水库库容为约束的水资源配置模型。通过输入郑州市各计算单元和用水部门的用水需求量和可用水量,该模型计算并输出郑州市9个区在2019年、2035年的缺水率。结果表明:郑州市供水的区域分布比较均衡,缺水率在可接受范围内;该模型算法进化速度较快,进化的稳定性较优,优化结果在种群中可以很好地保留且对进化方向的主导性很强,可以有效地应用于解决水资源配置问题,并提升模型计算效率,为水资源管理部门提供技术支持。

基于分组学习粒子群算法的众包软件项目调度

为解决众包软件项目调度问题中的开发者选择、任务分配和投入度确定3个强耦合子问题,引入开发者信誉度,考虑技能、工作时长、开发团队规模等约束,以项目完成质量和工期为目标建立数学模型。提出一种采用三段式混合编码的分组学习粒子群算法求解所建模型。所提算法根据适应度排序将种群划分为3组,不同分组的粒子数量随进化代数自适应变化,且各组根据不同的适应度采用不同的更新策略。将所提算法与10种具有代表性的算法在12个不同规模的众包软件项目调度算例中进行对比,结果表明,所提算法能够获得精度更高的调度方案。

基于双指标分组学习粒子群算法的动态敏捷软件项目调度

针对敏捷软件开发中的用户故事选择和任务分配2个紧耦合子问题,考虑用户故事的新增和开发者工作时长的不确定性,构建敏捷软件项目的动态周期性调度模型,提出一种基于目标值和潜力值双指标进行分组学习的粒子群优化算法。该算法依据不同分组特征选用相异的学习对象,以提高搜索的多样性;基于投资回报率和时间利用率设计初始化和局部搜索策略,以应对环境变化并增强挖掘能力。与7种已有算法相比,所提算法能够规划出一套产出价值更大和时间利用率更高的调度方案。

基于混合威布尔分布的水稻插秧机的可靠性分析及剩余寿命预测

为了更准确描述水稻插秧机的失效规律,提高可靠性分析的准确性,对水稻插秧机的故障数据进行分析,采用两参数混合威布尔分布对水稻插秧机进行建模。以残差平方和最小为优化目标,建立参数估计优化模型,利用改进粒子群算法对其进行求解,然后采用K-S检验法对模型进行检验,对比单一威布尔模型、混合威布尔模型与水稻插秧机失效数据之间的拟合程度,得出使用两参数混合威布尔模型评估水稻插秧机可靠性的合理性,在此模型的基础上计算得到水稻插秧机的平均无故障工作时间为161.75 h,中位寿命为147.14 h,特征寿命为191.31 h,且在可靠度为0.6时,预防性维修周期为115.19 h,最后在混合威布尔分布模型的基础上计算出剩余寿命-可靠度的关系,可定量分析插秧机在一定使用时间下的剩余寿命,从而进行预测性维护。