Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks

Strumberger, Ivana; Minović, Miroslav; Tuba, Milan; Bačanin, Nebojša

doi:10.3390/s19112515

Cited by 100 publications

(49 citation statements)

References 57 publications

(77 reference statements)

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“…It has to be noted that the authors have conducted research with improved and hybridized swarm intelligence algorithms before [31,32], and that they have also implemented some swarm intelligence approaches for resource scheduling tasks in cloud computing environment [4,33]. Hence, the research presented in this paper is the result of authors previous experience in this domain, as well as their recent work with WOA metaheuristic and resource scheduling problems in cloud computing.…”

Section: Objectives Contributions Question and Methodsology Of The Pmentioning

confidence: 99%

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Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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Section: Objectives Contributions Question and Methodsology Of The Pmentioning

confidence: 99%

“…In addition to the mentioned swarm intelligence algorithms, many others have also proven to be efficient optimization approaches, for example elephant herding optimization (EHO) [5,7,31,32,77], the fireworks algorithm (FWA), [78][79][80], and brain storm optimization (BSO) [81].…”

Section: Swarm Intelligence Overview and Cloud Computing Applicationsmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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“…The elephant herding optimization (EHO) also belongs to the group of novel swarm algorithms [69]. According to the literature survey, many practical EHO implementations can be found [5,[70][71][72]. In addition, modified versions of the EHO that were tested on standard benchmark functions exist [73].…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

“…Besides all mentioned above, there are also other state-of-the-art swarm intelligence algorithms that showed outstanding performance for tackling various kinds of practical problems, for example ant colony optimization (ACO) [83], brain storm optimization (BSO) [84][85][86], krill herd (KH) [87] algorithm, tree growth algorithm (TGA) [5,88,89], and many others [90][91][92][93].…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

“…Since the metaheuristics can efficiently handle massive search space, and, since they proved to be robust NP-hard problem solvers [5,6], in the area of tasks cloud scheduling, metaheuristic methods like swarm intelligence may obtain better results than other proposed approaches. Moreover, due to the fact that many real-life problems such as wireless sensor networks (WSNs) node localization, transportation problems, portfolio optimization, etc., can be modeled as optimization tasks, metaheuristic algorithms are widely implemented for addressing many practical NP-hard challenges, as can be seen from the literature.…”

Section: Introductionmentioning

confidence: 99%

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Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

Saravanan

2021

Int J Communication

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Summary The sensor nodes localization is very advantageous in wireless sensor network (WSN). This allows effective data transfer between the sensor node networks. Therefore, it saves energy and prolongs network life. Here, a hybrid FWCELM‐CPO method is proposed for solving the sensor node localization problem in WSN. The proposed hybrid method is executed in MATLAB, and the performance is analyzed with different existing algorithms like centroid, fuzzy centroid, and ELM. The simulation results show that the proposed FWCELM‐CPO method attains the higher detection rate of 14.117%, 5.435%, and 11.494%, higher segmentation accuracy of 9.556%, 26.41%, and 16%, lower execution time of 66.667%, 75%, and 70.37%, higher segmented region of 65.957%, 20%, and 44.444%, and higher precision of 34.72%, 18.29%, and 8.78% compared to the existing algorithms. The simulation results demonstrate that the proposed method can be able to find the optimal global solutions efficiently with accurately.

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Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks

Cited by 100 publications

References 57 publications

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

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