Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Codebook design using improved particle swarm optimization based on selection probability of artificial bee colony algorithm

In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.

基于粒子-人工蜂群算法的3RPUP_(c)-UPS并联机构运动学正解研究

针对3RPUP_(c)-UPS并联机构运动学正解求解困难的问题,对新型3RPUPc-UPS并联机构的运动学特性进行了研究,并构建出位置正解求解模型,进而提出了一种基于粒子-人工蜂群算法(P-ABC)的并联机构运动学求解方法。首先,根据机构的拓扑特性,计算得到了方位特征集、自由度和耦合度;然后,根据机构的几何特征,基于姿态变换矩阵和动平台投影方程,建立了机构的运动学逆解方程,并对比了MATLAB和SOLIDWORKDS的仿真结果,验证了逆解分析的正确性;最后,将运动学逆解方程转化为最小化求解问题,构建出了适合优化算法的运动学正解模型,并利用MATLAB的软件交互界面(GUI)功能,开发出用于计算并联机构运动学正解的软件,分别基于粒子群算法(PSO)、人工蜂群算法(ABC)和P-ABC算法,对该并联机构的运动学正解进行了计算。研究结果表明:P-ABC算法单次求解时间在0.5 s内,求解误差级别为10-20,相对于ABC算法,运行时间缩短了50.02%;而相对于POS算法,其求解精度提高了10个数量级。P-ABC算法能够用于求解该并联机构运动学正解,具有计算速度快、精度高的特点,可以为研究并联机构运动学正解提供新方法。

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.

IABC-PSO算法优化调度水库群应用研究

随着经济增长、城市化发展,北方地区水资源需求量迅速增加。如何通过水利工程对水资源进行人工调节,在满足用水需求的基础上,兼顾发电、生态等多目标是目前的研究热点之一。本文以山西大水网中的忻州—阳泉供水区为研究对象,建立区域梯级水库多目标优化调度模型,考虑供水区年总缺水最小、梯级水库年发电量最大和年总生态缺水率最小的综合目标,提出改进的人工蜂群-粒子群优化算法(IABC-PSO),避免传统粒子群算法的不足,并成功应用于研究区供水水库调度方案优化,结果表明,当地供水丰水年能满足需求,平水年和枯水年存在一定程度缺水。相较于粒子群算法(PSO)和人工蜂群算法(ABC),本方法优化调度后,区域供水缺水率和生态缺水率有所降低。该研究成果可为不同地区的水资源调配提供借鉴。

Algorithms for the Optimization of Well Placements—A Comparative Study

The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is very efficient and as promising as it is;it can be favourably compared to other optimization algorithms and in some cases, it has been proven to be better than some known algorithms (like Particle Swarm Optimization (PSO)), especially when used in Well placement optimization problems that can be encountered in the Petroleum industry. In this paper, the ABC algorithm has been modified to improve its speed and convergence in finding the optimum solution to a well placement optimization problem. The effects of variations of the control parameters for both algorithms were studied, as well as the algorithms’ performances in the cases studied. The modified ABC (MABC) algorithm gave better results than the Artificial Bee Colony algorithm. It was noticed that the performance of the ABC algorithm increased with increase in the number of its optimization agents for both algorithms studied. The modified ABC algorithm overcame the challenge posed by the use of uniformly generated random numbers with very rough NPV surface. This new modified ABC algorithm proposed in this work will be a great tool in optimization for the Petroleum industry as it involves Well placements for optimum oil production.

局部阴影下光伏阵列的最大功率点跟踪算法研究

针对局部阴影下光伏阵列输出功率的多峰值问题,传统的MPPT跟踪算法不能准确跟踪系统的最大功率点,为此,该文研究了3种基于人工智能算法的光伏阵列MPPT算法,包括粒子群算法、灰狼算法和改进人工蜂群算法。该文详细介绍了3种人工智能算法的原理及算法流程,并在Matlab/Simulink中搭建系统的仿真模型,对比3种算法在静态阴影遮挡和阴影突变情况下的MPPT跟踪性能,结果表明:3种人工智能算法均能有效跟踪光伏阵列的最大功率点,跟踪误差均小于0.5%,其中粒子群算法跟踪精度最高,收敛速度最慢,而灰狼算法跟踪精度最低,收敛速度最快,在收敛稳定性方面,相较于灰狼算法和改进人工蜂群算法,粒子群算法更易陷入局部最优。

Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

Maximum likelihood estimation(MLE)is an effective method for localizing radioactive sources in a given area.However,it requires an exhaustive search for parameter estimation,which is time-consuming.In this study,heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors.Hence,the time consumption of MLE would be effectively reduced.First,the radiation source was detected using the k-sigma method.Subsequently,the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source.A comparative study was performed in which the estimation accuracy and time consump-tion of the MLE were evaluated for traditional methods and heuristic techniques.The traditional MLE was performed via a grid search method using fixed and multiple resolutions.Additionally,four commonly used heuristic algorithms were applied:the firefly algorithm(FFA),particle swarm optimization(PSO),ant colony optimization(ACO),and artificial bee colony(ABC).The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program.The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE.The time consumption for the heuristic-based MLE was 0.75,0.03,0.02,and 0.059 s for FFA,PSO,ACO,and ABC,respectively.The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE.Hence,heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.

整流电路晶闸管RBF神经网络故障诊断研究

为有效诊断和识别三相桥式全控整流电路中晶闸管故障,提高故障诊断精准度,采用对整流电路输出电压的采样数据进行晶闸管故障特征提取,建立晶闸管故障诊断的RBF神经网络模型。采用改进粒子群-人工蜂群算法优化训练RBF神经网络。仿真实验分析表明,RBF神经网络的实际输出与期望输出完全一致,具有良好的故障诊断识别效果,故障诊断准确率高、可靠性强,具有较强的工程实践指导价值。

人工蜂群算法优化SVR的预测模型

针对人工蜂群算法存在的易陷入局部最优、收敛速度慢的缺点,引入当前最优食物源和惯性权重函数,对该算法的食物源更新方式进行改进;针对支持向量回归机的参数优化问题,将其转化为组合优化问题,并使用改进的人工蜂群算法进行优化求解,进而得到人工蜂群算法优化SVR的预测模型。以短期交通流量数据为例,将该模型的预测结果与蚁群算法优化的支持向量回归机(ACO-SVR)、粒子群算法优化的支持向量回归机(PSO-SVR)和未改进的蜂群算法优化的支持向量回归机(ABC-SVR)进行对比分析,结果表明该模型的预测效果最优且运行时间最短,具有更好的学习能力和推广能力。

具有混合群智能行为的萤火虫群优化算法研究

萤火虫群优化算法是一种新型的群智能优化算法,基本的萤火虫群优化算法存在收敛精度低等问题。为了提高算法的性能,借鉴蜂群和鸟群的群体智能行为,改进萤火虫群优化算法的移动策略。运用均匀设计调整改进算法的参数取值。若干经典测试问题的实验仿真结果表明,引入混合智能行为大幅提升了算法的优化性能。

群智能算法的理论及应用综述

群智能是由自然或人造的分散自组织系统所表现出来的集体智能.群智能包含一组简单的个体,其中个体与个体、个体与环境之间存在局部交互行为.虽然个体遵循非常简单的规则,但是微观的交互最终还是导致了宏观的智能行为.在本文中,我们对典型群智能方法的起源、发展、理论、技术、应用等做了深入的研究,包括了蚁群优化、粒子群优化、人工蜂群、细菌觅食优化、萤火虫共五类算法.文末提出群智能发展的六个方向.

PSO和ABC的混合优化算法

通过将粒子群优化(Particle Swarm Optimization,PSO)算法与人工蜂群(Artificial Bee Colony,ABC)算法相结合,提出一种ABC-PSO并行混合优化算法。在每次迭代中,将种群分为两个子种群,一个子种群使用PSO算法,另一个子种群使用ABC算法,两个算法寻优后进行比较,选出最优适应值。通过混合算法对4个标准函数进行测试,并与标准PSO算法进行比较,结果表明混合算法具有更好的优化性能。

多种群粒子群与人工蜂群融合的改进算法

为解决粒子群算法在解决多峰问题时容易陷入局部最优的问题,对粒子群算法和人工蜂群算法进行研究,提出一种新的融合算法。采用多种群粒子群方法进化,每次进化后将各子群中的最优粒子重新组合一个新的群体,利用人工蜂群模式进化得到全局最优个体;将全局最优个体反馈到粒子群各子群的进化模式中,以提高算法的收敛速度。将10个测试函数的仿真结果与一些改进的粒子群和标准人工蜂群算法进行了比较,比较结果表明,融合算法有7个测试函数的测试效果最好,其中4个为单峰函数,3个为多峰函数;该算法具有良好的全局搜索能力和较快的收敛速度。

Hadoop平台下粒子滤波结合改进ABC算法的IoT大数据特征选择方法

针对现有物联网大数据特征选择算法计算效率低下、可扩展性不高的问题,提出一种基于改进人工蜂群(ABC)选择特征的系统架构,该架构包含四层体系,可以高效地聚合有效数据,剔除不需要的数据。整个系统是基于Hadoop平台、MapReduce以及改进ABC算法的。改进ABC算法用于选择特征,而MapReduce则由并行算法支持,该算法可高效处理大数据集。该系统使用MapReduce工具实现,并利用粒子滤波来消除噪声。将提出的算法与同类方法进行比较,并通过使用十个不同的数据集对效率、准确性和吞吐量进行评估。结果表明,相比其他几种较新的算法,提出的算法在选择特征时更具可扩展性和高效性。

基于群智能混合算法的应急物流路径优化研究

应急物流系统是解决突发事件的有效框架体系,如何选择合适的配送路径以确保受灾群众及时获取物资,对解决应急救援问题有重要意义。通过建立应急物流路径优化模型,考虑到使用人工萤火虫算法会产生寻优精度低以及陷入局部最优等问题,为了提高系统优化性能,参照蜂群和粒子群的群体移动规律,改进萤火虫算法的位置更新策略,引入两种群智能混合算法进行比较实验。设置距离参数和平均交通复杂度,计算物流运输路径系统消耗时间,并采取表格形式显示。实验结果表明群智能混合算法能大幅度优化应急物流路径规划模型,提高配送效率。

基于BCABC-SVM的边坡稳定性预测

为了准确地对边坡稳定性进行预测,采用支持向量机(SVM)建立边坡稳定性和影响因素之间的非线性关系.针对支持向量机参数对预测效果的影响,采用基于细菌趋化的蜂群算法(BCABC)对其进行优化选择,提出了边坡稳定性预测的细菌趋化的蜂群优化支持向量机模型.运用该方法对边坡实例进行预测,预测结果与边坡稳定性实际状态相吻合,结果表明,基于细菌趋化的蜂群优化支持向量机模型在边坡稳定性评价中具有一定的可靠性和有效性.

全局最优引导的差分演化二进制人工蜂群算法

针对基本二进制人工蜂群算法开采能力弱、收敛速度慢的缺点,提出一种全局最优引导的差分二进制人工蜂群算法。算法仿照粒子群优化,将全局最优参数引入二进制人工蜂群算法中以提高开采能力;同时受差分演化算法中"交叉"操作的启发,提出多维邻域搜索方式,加快收敛速度。采用0-1背包问题进行仿真,实验结果表明与传统算法相比,提出算法不仅寻优能力增强且收敛速度明显提高。对于10维背包问题,提出算法的收敛速度比基本二进制人工蜂群算法提高近10倍。

嵌入粒子群优化算法的混合人工蜂群算法

为了克服人工蜂群算法存在的早熟收敛、后期收敛速度变慢等缺点,提出了一种基于粒子群优化算法的混合人工蜂群算法(PABC)。对陷入局部极值的雇佣蜂,采用粒子群优化算法对其重新进行初始化。粒子群优化算法具有很强的全局搜索性能,能使陷入局部极值的雇佣蜂尽快摆脱局部约束。测试函数的计算结果表明,改进的人工蜂群算法大大提高了蜂群算法的寻优能力,在收敛速度和精度方面均优于基本蜂群算法。

群智能算法在螺旋桨参数优化设计中的应用

螺旋桨参数优化设计一般是复杂的非线性问题,设计的难点在于如何在各种非线性约束条件下找到一组适当的参数,使得螺旋桨性能最佳。群智能算法作为一种新兴演化计算技术,能有效解决全局优化问题,是优化算法研究的新热点。首先介绍了粒子群算法和蜂群算法两种群智能算法的工作原理;然后在建立螺旋桨参数优化数学模型的基础上,将群智能算法运用到螺旋桨初步和终结设计优化问题中,并通过实例进行对比分析,结果表明群智能算法解决螺旋桨参数优化问题是实用且高效的。