Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing

Amer, Dina A.; Attiya, Gamal; Zeidan, Ibrahim; Nasr, Aida A.

doi:10.1007/s11227-021-03977-0

Cited by 45 publications

(18 citation statements)

References 32 publications

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“…In certain circumstance, the crow goes a further step and steal the neighbor's food. Amer et al [16] introduce an adapted Harris hawk's optimizer (HHO), named elite learning HHO (ELHHO), for multiobjective scheduling issues. The modification is performed by utilizing a smart technology named elite OBL for enhancing the superiority of exploration stage of the typical HHO approach.…”

Section: Related Workmentioning

confidence: 99%

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Chellapraba¹,

Manohari²,

Periyakaruppan³

et al. 2023

Computer Systems Science and Engineering

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Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand. Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities. CC was handled as a resource-rich solution to aforementioned challenge. As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user job. Therefore, this article concentration on the design of an oppositional red fox optimization based task scheduling scheme (ORFO-TSS) for IoT enabled cloud environment. The presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud platform. It achieves the makespan by performing optimum TS procedures with various aspects of incoming task. The designing of ORFO-TSS method includes the idea of oppositional based learning (OBL) as to traditional RFO approach in enhancing their efficiency. A wide-ranging experimental analysis was applied on the CloudSim platform. The experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

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Section: Related Workmentioning

confidence: 99%

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Chellapraba¹,

Manohari²,

Periyakaruppan³

et al. 2023

Computer Systems Science and Engineering

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“…Amer et al [132] developed another version of the HHO called Elite Learning HHO (ELHHO). They used elite opposition-based learning to improve exploration phase quality.…”

Section: Opposite Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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“…In 2019, the Harris Hawks Optimizer (HHO) was introduced as a new heuristic population-based approach for optimization problems [24,25]. The main notion of this technique is emulated by the cooperative actions of smart Harris' Hawks birds while hunting a prey.…”

Section: Harris Hawks Optimizer (Hho)mentioning

confidence: 99%

A new cloud autonomous system as a service for multi-mobile robots

Nasr

2022

Neural Comput & Applic

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Today, mobile robot is used in most industrial and commercial fields. It can improve and carry out work complex tasks quickly and efficiently. However, using swarm robots to execute some tasks requires a complex system for assigning robots to these tasks. The main issue in the robot control systems is the limited facilities of robot embedded system components. Although, some researchers used cloud computing to develop robot services. They didn’t use the cloud for solving robot control issues. In this paper, we have used cloud computing for controlling robots to solve the problem of limited robot processing components. The main advantage of using cloud computing is its intensive computing power. This advantage motivates us to propose a new autonomous system for multi-mobile robots as a services-based cloud computing. The proposed system consists of three phases: clustering phase, allocation phase, and path planning phase. It groups all tasks/duties into clusters using the k-means algorithm. After that, it finds the optimal path for each robot to execute its duties in the cluster based on the Nearest neighbor and Harris Hawks Optimizer (HHO). The proposed system is compared with systems that use a genetic algorithm, simulated annealing algorithm, and HHO algorithm. From the finding, we find that the proposed system is more efficient than the other systems in terms of decision time, throughput, and the total distance of each robot.

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Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing

Cited by 45 publications

References 32 publications

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

A new cloud autonomous system as a service for multi-mobile robots

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