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.

Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)

The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.

Cooperative Search of UAV Swarm Based on Ant Colony Optimization with Artificial Potential Field

An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.

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.

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends

Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

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.

Traveling Salesman Problem Using an Enhanced Hybrid Swarm Optimization Algorithm

The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.

Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.

A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

考虑电动汽车充电负荷及储能寿命的充电站储能容量配置优化

提出了一种优化电动汽车充电站储能容量配置的方法。该方法考虑了季节性电动汽车充电负荷波动与光伏出力之间的关系,并且考虑了储能寿命。论文利用蒙特卡罗法考虑了不同类型电动汽车的多种影响因素,对整体负荷进行预测。以每日运行成本最低为优化目标,在考虑四季光伏出力和储能寿命的影响下,采用了3种算法对目标函数进行优化,以得到最佳的光储充电站储能配置方案。研究以西北某地区为例。结果表明:冬季下综合成本为3.0432×10^(6)元,相比于其余3个季节综合成本最低;采用遗传算法时,在综合成本相差不多时,获得的储能配置最优,储能容量为22.82 MWh,储能功率为7.31MW,从而得到光储充电站最优的储能容量配置。

基于AIS轨迹和改进蚁群算法的船舶航线规划方法

在保证船舶航线安全的前提下,以最短航程为目标,提出基于AIS轨迹和改进蚁群算法的船舶航线规划方法。对船舶AIS数据进行预处理,去除船舶AIS数据中的冗余数据,完成船舶AIS数据提纯;采用基于粒子群与K均值混合聚类算法的核心转向点筛选与识别方法,筛选并识别船舶AIS数据中船舶航线核心转向点数据;通过基于改进蚁群算法的航线规划方法,以核心转向点数据为基础,构建航线网络,在此网络中,通过人工势场法对蚁群算法进行改进,对船舶航线进行寻优,实现船舶航线规划。经实验验证,本文方法能够规划出安全合理的船舶航线。

陷阱标记联合懒蚂蚁的自适应粒子群优化算法

为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。

IACO-GA-IPSO融合算法AUV三维全局路径规划

为了解决传统蚁群算法收敛速度慢,易陷入局部最优,传统粒子群算法搜索精度差,初始路径不规则等问题,提出一种融合了改进蚁群算法(IACO)、改进粒子群算法(IPSO)和遗传算法(GA)的IACO-GA-IPSO路径规划算法。首先定义三维海洋环境模型,将工作空间沿Z轴方向划分成水平的栅格平面;其次建立多标准的路径优劣评价模型;最后由融合算法规划路径:IACO算法生成次优种群,GA算法优化种群多样性,IPSO算法快速收敛到全局最优。实验结果表明,融合算法能充分发挥每种算法的优点,克服种群规模和收敛速度的矛盾,优化初始种群,提高全局搜索能力、局部搜索精度和算法运行效率,加快收敛速度并避免陷入局部最优路径。

改进蚁群算法在地形跟随航线规划问题中的应用

针对飞机地形跟随航线规划需要,提出了一种基于改进蚁群算法的通用解决方案。该方法通过空间等分的思想将三维地图重构为解空间,并通过一系列改进措施提升蚁群算法效率,包括围绕加强蚁群中最优蚂蚁的正增益、减弱最劣蚂蚁的负增益,设计信息素更新策略;综合考虑可行航路点距离、高度、转弯角度的影响,设计节点移动策略;采用粒子群算法,智能优化求解蚁群算法的核心参数等,实现地形跟随航线的快速生成。通过具体算例验证了该方法的先进性和可行性。

基于粒子群和改进蚁群算法的云计算任务调度

针对目前云计算任务调度方法的效率较低和日益多样化的用户服务质量需求等问题,提出一种将粒子群算法和改进蚁群算法结合的混合粒子群蚁群算法(HPSO-ACO),包括建立云计算任务调度模型、用户服务质量模型及虚拟资源节点模型。利用离散型粒子群算法,得到初始解集,转化为蚁群算法信息素的初始值,通过改进蚁群算法的寻径规则和信息素更新规则,得到最终解。通过仿真实验将粒子群算法、蚁群算法和HPSO-ACO算法进行比较,其结果表明,HPSO-ACO算法有效且可行,能够减少任务完成时间和降低完成成本,满足用户服务质量要求。

面向无人机航路的优化算法研究综述

随着无人机任务复杂性以及环境不确定性的不断提高,对航路规划的要求也随之提高,航路规划问题复杂度逐渐增加,由单无人机航路规划向多无人机规划发展,由单任务向多任务发展。针对无人机航路规划问题,从概念内涵、任务建模、算法解析等方面进行了综合分析。针对现有航路规划算法存在的最优路径效果较差、收敛速度慢以及易陷入局部最优等问题,重点分析了A*算法、粒子群算法、遗传算法、蚁群算法在无人机航路规划中的应用及存在的问题,提出了优化改进的方向。

地震场景下无人机群路径规划与任务分配均衡联合优化

无人机(UAV)群路径规划和任务分配是UAV群救援应用的核心,然而传统方法分开求解路径规划与任务分配,导致资源分配不均。为了解决上述问题,结合UAV群的物理属性与应用环境因素,改进蚁群算法(ACO),提出联合并行蚁群(JPACO)模型。首先,借助分级信息素增强系数机制更新信息素,以提高JPACO任务分配均衡性和能耗均衡性;其次,设计路径平衡因子和动态概率转移因子优化蚁群模型易陷入局部收敛的情况,从而提高JPACO的全局搜索能力;最后,引入集群并行处理机制,以降低JPACO运算耗时。将JPACO与自适应动态蚁群算法(ADACO)、扫描动态蚁群算法(SMACO)、贪婪策略蚁群算法(GSACO)和交叉蚁群算法(IACO)在公开数据集CVRPLIB上对比最优路径、任务分配均衡、能耗均衡和运算耗时。实验结果表明:与IACO和ADACO相比,JPACO处理小规模运算的最优路径平均值分别降低7.4%和16.3%;处理大规模运算的求解耗时与GSACO、ADACO相比降低8.2%和22.1%。以上结果验证了JPACO在处理小规模运算时能够改善最优路径,处理大规模运算时任务分配均衡、能耗均衡和运算耗时明显优于对比算法。

基于蚁群优化算法的无线传感器网络节能路由策略

针对无线传感器网络,设计一种基于蚁群优化算法的无线传感器网络节能路由策略。考虑无线传感器网络具有带宽有限、电能有限、内存低等限制性,基于元启发式影响因子对WSN路由进行建模,模拟蚂蚁的觅食行为进行路由搜索,计算转发路径。通过引入期望跳数、信号接收强度指示、剩余能量等,并综合信息素对下一跳概率函数进行设计,从而实现跳数优化、节能优化的目的,仿真证明了该路由策略的有效性。

基于智能优化方法的工业机器人时间最优轨迹规划方法

随着工业自动化进程的加速,工业机器人的轨迹规划问题愈发凸显其重要性。由于传统轨迹规划方法在时间最优性方面的局限性,本文致力于研究基于智能优化算法的工业机器人时间最优轨迹规划。本文阐述了轨迹规划的定义并建立了相应的数学模型。通过对比传统方法与智能优化算法,重点探讨了基于粒子群优化和蚁群优化的时间最优轨迹规划方法,分别描述了问题并提出了求解策略。研究结果显示,智能优化算法在提升工业机器人运动效率和精度方面具有显著优势。