A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

Zheng, Jianguo; Wang, Yilin

doi:10.3390/su13147933

Cited by 16 publications

(8 citation statements)

References 34 publications

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“…Traditional optimization methods have several drawbacks when solving complex and complicated problems that require considerable time and cost optimization. Metaheuristic algorithms have been proven capable of handling a variety of continuous and discrete optimization problems [46] in a wide range of applications including engineering [47][48][49], industry [50,51], image processing and segmentation [52][53][54], scheduling [55,56], photovoltaic modeling [57,58], optimal power flow [59,60], power and energy management [61,62], planning and routing problems [63][64][65], intrusion detection [66,67], feature selection [68][69][70][71][72], spam detection [73,74], medical diagnosis [75][76][77], quality monitoring [78], community detection [79], and global optimization [80][81][82]. In the following, some representative metaheuristic algorithms from the swarm intelligence category used in our experiments are described.…”

Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

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Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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“…This enlargement is accompanied by a shift from exploration mode to local intensive exploitation. BA also has been used for many applications, for example travelling salesman problem [18,19], resource scheduling [20,21], customer churn [22,23], brain tumor recognition [24,25], estimating state of health of lithium-ion batteries [26], detection of myocardial infarction [27] and features selection [28,29]. www.ijacsa.thesai.org Based on the background described, this article proposes the development of a method for diagnosing diabetes mellitus based on the K-means clustering algorithm optimized by BA.…”

Section: Introductionmentioning

confidence: 99%

Classification Model for Diabetes Mellitus Diagnosis based on K-Means Clustering Algorithm Optimized with Bat Algorithm

Anam¹,

Fitriah²,

Hidayat³

et al. 2023

IJACSA

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Diabetes mellitus is a disease characterized by abnormal glucose homeostasis resulting in an increase in blood sugar. According to data from the International Diabetes Federation (IDF), Indonesia ranks 7th out of 10 countries with the highest number of diabetes mellitus patients in the world. The prevalence of patients with diabetes mellitus in Indonesia reaches 11.3 percent or there are 10.7 million sufferers in 2019. Prevention, risk analysis and early diagnosis of diabetes mellitus are necessary to reduce the impact of diabetes mellitus and its complications. The clustering algorithm is one of methods that can be used to diagnose and analyze the risk of diabetes mellitus. The K-mean Clustering Algorithm is the most commonly used clustering algorithm because it is easy to implement and run, computation time is fast and easy to adapt. However, this method often gets to be stuck at the local optima. The problem of the Kmeans Clustering Algorithm can be solved by combining the Kmeans Clustering algorithm with the global optimization algorithm. This algorithm has the ability to find the global optimum from many local optimums, does not require derivatives, is robust, easy to implement. The Bat Algorithm (BA) is one of global optimization methods in swarm intelligence class. BA uses automated enlargement techniques into a solution and it's accompanied by a shift from exploration mode to local intensive exploitation. Based on the background that has been explained, this article proposes the development of a classification model for diagnosing diabetes mellitus based on the K-means clustering algorithm optimized with BA. The experimental results show that the K-means clustering optimized by BA has better performance than K-means clustering in all metrics evaluations, but the computational time of the K-means clustering optimized by BA is higher than K-means clustering.

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“…Aimed to improve response time as well as minimize VM failure rate, a multi-objective genetic algorithm for load balancing in a mobile cloud-computing environment was improved, and it performed well by reducing execution time and task wait time at the server (Ramasubbareddy et al, 2021). To improve the service quality of cloud computing, a hybrid multi-objective bandwidth aware divisible (BAT) algorithm, based on the mean square error and the conjugate gradient method, was proposed, and obtained a slightly better cost than the multiobjective genetic algorithm (Zheng & Wang, 2021). These bionic intelligent algorithms can be used to optimize the relative resource scheduling problems to improve resource utilization effectively for the capability of searching and high parallelism.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bat Algorithm With Time-Varying Wavelet Perturbations for Cloud Computing Resources Scheduling

Yu¹,

Chen²,

2023

International Journal of Cognitive Informatics and Natural Intelligence

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Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the best balance between speed of operation, average response time, and integrated system utilization in the resource allocation process in cloud computing, an improved bat algorithm with time-varying wavelet perturbations was proposed. The algorithm provided a perturbation strategy of time-varying Morlet wavelet with the waving property to prevent from local optimum greatly and improve the converging speed and accuracy through the guide of individual distribution to control diversity and time-varying coefficient of wavelets. The experiments showed the proposed could significantly upgrade the overall performance and the capability of resource scheduling in cloud service compared to similar algorithms.

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A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

Cited by 16 publications

References 34 publications

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

Classification Model for Diabetes Mellitus Diagnosis based on K-Means Clustering Algorithm Optimized with Bat Algorithm

An Improved Bat Algorithm With Time-Varying Wavelet Perturbations for Cloud Computing Resources Scheduling

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