Triangle分层护理管理对高血压患者血压水平和依从性的影响

目的 分析Triangle分层护理管理对高血压患者的影响。方法 抽取2019年6月至2022年12月共78例高血压住院患者作为研究对象,应用随机抽样法划分为两组,对照组中提供常规护理管理(n=39),研究组提供Triangle分层护理管理(n=39),对比分析两组患者血压、依从率、自我管理行为、并发症。结果 患者管理前收缩压和舒张压显示无差异(P>0.05),管理后研究组低于照组,差异具有统计学意义(P<0.05);自我管理行为评分对比,管理前比较两组无差异(P>0.05),管理后升高,经对比,研究组评分高于另一组,差异具有统计学意义(P<0.05);依从率对比差异具有统计学意义(P<0.05),对照组(79.49%)低于研究组(94.87%);并发症发生率经对比差异具有统计学意义(P<0.05),研究组(2.56%)更低。结论 Triangle分层护理管理对高血压患者具有重要影响,能积极有效地控制血压,提高其治疗依从性、自我管理行为,并能降低并发症。

以Triangle模型为基础的分级护理应用于糖尿病足患者的效果

目的观察并分析以Triangle模型为基础的分级护理应用于糖尿病足患者的效果。方法选取漯河市中心医院2021年12月至2023年12月收治的97例糖尿病足患者,按照抽签法分组,常规组48例接受常规护理,研究组49例患者接受以Triangle模型为基础的分级护理,均干预3个月后,比较两组溃疡修复情况、神经传导速度、自我效能、并发症及生活质量。结果研究组感觉检查、症状以及反射评分低于常规组(P<0.05)。研究组胫神经、腓总神经传导速度快于常规组(P<0.05)。研究组3个月后自我减压、自我决策、正性态度及生活质量评分均高于常规组(P<0.05)。研究组10 g尼龙绳触觉、痛觉、冷热觉及振动觉异常和下肢麻木发生率低于常规组(P<0.05)。两组皮肤干燥情况差异无统计学意义(P>0.05)。结论以Triangle模型为基础的分级护理可明显提高糖尿病足患者自我效能,有利于其溃疡修复和神经传导速度改善,减少并发症发生并促进生活质量的提高。

糖尿病肾病开展Triangle分层分级管理+LEARNS模式的效果及心理状态的观察

目的探究在对糖尿病肾病患者护理时开展Triangle分层分级管理+LEARNS模式对患者血糖水平、肾功能水平以及心理状态等改善作用。方法在2022年2月至2023年1月云南省第三人民医院收治糖尿病肾病患者中随机抽选74例为对象,Excel表随机排序划分对照组(n=37,治疗期间接受常规护理)和观察组(n=37,接受Triangle分层分级管理+LEARNS模式护理)。针对患者血压水平、肾功能水平等改善情况进行评估。结果对比2组干预前后血糖水平,餐后2 h血糖、空腹血糖,干预后观察组低于对照组,差异有统计学意义(P<0.05)。对比患者生存质量,干预前无差异,干预后观察组QOLIE-31量表评分高于对照组,差异有统计学意义(P<0.05)。对比患者负面情绪评分,干预前BAI以及BDI评分无差异,干预后观察组评分低于对照组,差异有统计学意义(P<0.05)。对比患者肾功能水平,干预后观察组肾功能水平高于对照组,差异有统计学意义(P<0.05)。对比2组自我管理能力,干预后观察组高于对照组,差异有统计学意义(P<0.05)。结论在对糖尿病肾病患者护理时开展Triangle分层分级管理+LEARNS模式,可改善患者血糖、肾功能水平,缓解患者治疗期间负面情绪,有助于患者进行恢复。

TRIANGLE operation,combined with adequate adjuvant chemotherapy,can improve the prognosis of pancreatic head cancer:A retrospective study

BACKGROUND The TRIANGLE operation involves the removal of all tissues within the triangle bounded by the portal vein-superior mesenteric vein,celiac axis-common hepatic artery,and superior mesenteric artery to improve patient prognosis.Although previously promising in patients with locally advanced pancreatic ductal adenocarcinoma(PDAC),data are limited regarding the long-term oncological outcomes of the TRIANGLE operation among resectable PDAC patients undergoing pancreaticoduodenectomy(PD).AIM To evaluate the safety of the TRIANGLE operation during PD and the prognosis in patients with resectable PDAC.METHODS This retrospective cohort study included patients who underwent PD for pancreatic head cancer between January 2017 and April 2023,with or without the TRIANGLE operation.Patients were divided into the PD_(TRIANGLE)and PD_(non-TRIANGLE)groups.Surgical and survival outcomes were compared between the two groups.Adequate adjuvant chemotherapy was defined as adjuvant chemotherapy≥6 months.RESULTS The PD_(TRIANGLE)and PD_(non-TRIANGLE) groups included 52 and 55 patients,respectively.There were no significant differences in the baseline characteristics or perioperative indexes between the two groups.Furthermore,the recurrence rate was lower in the PD_(TRIANGLE) group than in the PD_(non-TRIANGLE) group(48.1%vs 81.8%,P<0.001),and the local recurrence rate of PDAC decreased from 37.8%to 16.0%.Multivariate Cox regression analysis revealed that PD_(TRIANGLE)(HR=0.424;95%CI:0.256-0.702;P=0.001),adequate adjuvant chemotherapy≥6 months(HR=0.370;95%CI:0.222-0.618;P<0.001)and margin status(HR=2.255;95%CI:1.252-4.064;P=0.007)were found to be independent factors for the recurrence rate.CONCLUSION The TRIANGLE operation is safe for PDAC patients undergoing PD.Moreover,it reduces the local recurrence rate of PDAC and may improve survival in patients who receive adequate adjuvant chemotherapy.

Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.

Hybrid Optimization Algorithm for Handwritten Document Enhancement

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

Effect of non-uniform swelling on coal multiphysics during gas injection: The triangle approach

In current dual porosity/permeability models,there exists a fundamental assumption that the adsorption-induced swelling is distributed uniformly within the representative elementary volume (REV),irrespective of its internal structures and transient processes.However,both internal structures and transient processes can lead to the non-uniform swelling.In this study,we hypothesize that the non-uniform swelling is responsible for why coal permeability in experimental measurements is not only controlled by the effective stress but also is affected by the adsorption-induced swelling.We propose a concept of the swelling triangle composed of swelling paths to characterize the evolution of the non-uniform swelling and serve as a core link in coupled multiphysics.A swelling path is determined by a dimensionless volumetric ratio and a dimensionless swelling ratio.Different swelling paths have the same start and end point,and each swelling path represents a unique swelling case.The swelling path as the diagonal of the triangle represents the case of the uniform swelling while that as the two perpendicular boundaries represents the case of the localized swelling.The paths of all intermediate cases populate inside the triangle.The corresponding relations between the swelling path and the response of coal multiphysics are established by a non-uniform swelling coefficient.We define this method as the triangle approach and corresponding models as swelling path-based ones.The proposed concept and models are verified against a long-term experimental measurement of permeability and strains under constant effective stress.Our results demonstrate that during gas injection,coal multiphysics responses have a close dependence on the swelling path,and that in both future experiments and field predictions,this dependence must be considered.

基于Triangle分层分级管理的LEARNS模式对妊娠期糖尿病孕期血糖控制水平及母婴结局

目的探讨基于Triangle分层分级管理的LEARNS模式对妊娠期糖尿病孕期血糖控制水平及母婴结局影响。方法选取山东省滕州市妇幼保健院2021年1月—2022年12月收治的116例妊娠期糖尿病孕妇,应用抽签法将孕妇分为观察组与对照组,每组58例。对照组实施常规护理,观察组实施基于Triangle分层分级管理的LEARNS模式干预。分别在干预前后应用一般自我效能量表(GSES)对两组孕妇的自我效能进行评价,应用健康促进生活方式量表Ⅱ(HPLPⅡ)评价两组孕妇生活健康水平,并对比两组产妇干预前后血糖水平及母婴结局。结果干预后,两组GSES、HPLPⅡ评分均升高,观察组GSES、HPLPⅡ评分高于对照组,差异有统计学意义(P<0.05);干预后,两组空腹血糖、餐后2 h血糖、糖化血红蛋白水平均降低,观察组空腹血糖、餐后2 h血糖、糖化血红蛋白水平低于对照组,差异有统计学意义(P<0.05);观察组妊娠不良结局、围产儿不良结局发生率明显低于对照组,差异有统计学意义(P<0.05)。结论基于Triangle分层分级管理的LEARNS模式可提升妊娠期糖尿病孕妇自我效能及健康生活水平,更有利于产妇孕期血糖控制,进而降低母婴不良结局发生率。

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

Multi-Objective Optimization of VBHF in Deep Drawing Based on the Improved QO-Jaya Algorithm

Blank holder force(BHF)is a crucial parameter in deep drawing,having close relation with the forming quality of sheet metal.However,there are different BHFs maintaining the best forming effect in different stages of deep drawing.The variable blank holder force(VBHF)varying with the drawing stage can overcome this problem at an extent.The optimization of VBHF is to determine the optimal BHF in every deep drawing stage.In this paper,a new heuristic optimization algorithm named Jaya is introduced to solve the optimization efficiently.An improved“Quasi-oppositional”strategy is added to Jaya algorithm for improving population diversity.Meanwhile,an innovated stop criterion is added for better convergence.Firstly,the quality evaluation criteria for wrinkling and tearing are built.Secondly,the Kriging models are developed to approximate and quantify the relation between VBHF and forming defects under random sampling.Finally,the optimization models are established and solved by the improved QO-Jaya algorithm.A VBHF optimization example of component with complicated shape and thin wall is studied to prove the effectiveness of the improved Jaya algorithm.The optimization results are compared with that obtained by other algorithms based on the TOPSIS method.

Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Comparison of prognosis and postoperative morbidities between standard pancreaticoduodenectomy and the TRIANGLE technique for resectable pancreatic ductal adenocarcinoma

BACKGROUND Radical surgery combined with systemic chemotherapy offers the possibility of long-term survival or even cure for patients with pancreatic ductal adenocar-cinoma(PDAC),although tumor recurrence,especially locally,still inhibits the treatment efficacy.The TRIANGLE technique was introduced as an extended dissection procedure to improve the R0 resection rate of borderline resectable or locally advanced PDAC.However,there was a lack of studies concerning postoperative complications and long-term outcomes of this procedure on patients with resectable PDAC.PDAC.METHODS Patients with resectable PDAC eligible for PD from our hospital between June 2018 and December 2021 were enrolled in this retrospective cohort study.All the patients were divided into PDstandard and PDTRIANGLE groups according to the surgical procedure.Baseline characteristics,surgical data,and postoperative morbidities were recorded.All of the patients were followed up,and the date and location of tumor recurrence,and death were recorded.The Kaplan-Meier method and log-rank test were used for the survival analysis.RESULTS There were 93 patients included in the study and 37 underwent the TRIANGLE technique.Duration of operation was longer in the PDTRIANGLE group compared with the PDstandard group[440(410-480)min vs 320(265-427)min](P=0.001).Intraoperative blood loss[700(500-1200)mL vs 500(300-800)mL](P=0.009)and blood transfusion[975(0-1250)mL vs 400(0-800)mL](P=0.009)were higher in the PDTRIANGLE group.There was a higher incidence of surgical site infection(43.2%vs 12.5%)(P=0.001)and postoperative diarrhea(54.1%vs 12.5%)(P=0.001)in the PDTRIANGLE group.The rates of R0 resection and local recurrence,overall survival,and disease-free survival did not differ significantly between the two groups.CONCLUSION The TRIANGLE technique is safe,with acceptable postoperative morbidities compared with standardized PD,but it does not improve prognosis for patients with resectable PDAC.

Triangle分层分级管理结合LEARNS模式在糖尿病中的应用

目的研究糖尿病患者采用Triangle分层分级管理结合LEARNS模式干预的护理效果。方法选择2020年10月—2023年10月东南大学附属中大医院收治的40例糖尿病患者为研究对象,以随机抽签法分为A组(20例,行Triangle分层分级管理结合LEARNS模式护理)和B组(20例,施行常规护理干预)。对比两组护理前后血糖水平、糖尿病自我管理行为量表(Summary of Diabetes Self Care Activities,SDSCA)评分及依从性。结果干预后,A组患者血糖水平显著优于B组,差异有统计学意义(P<0.05)。干预前,两组患者SDSCA评分对比,差异无统计学意义(P>0.05);干预后,A组SDSCA评分显著优于B组,差异有统计学意义(P<0.05)。A组患者依从性显著优于B组,差异有统计学意义(P<0.05)。结论给予糖尿病患者Triangle分层分级管理结合LEARNS模式进行干预,与常规护理干预对比,其能够很大程度改善患者血糖情况,提高糖尿病患者自我管理能力及依从性。

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

Hybrid Prairie Dog and Beluga Whale Optimization Algorithm for Multi-Objective Load Balanced-Task Scheduling in Cloud Computing Environments

The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.