A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems

Gaidhane, Prashant J.; Nigam, M. J.

doi:10.1016/j.jocs.2018.06.008

Cited by 90 publications

(36 citation statements)

References 37 publications

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“…The third level of wolves is sigma, which is the victims, and the fourth level is omega. The method of hunting such animals has been inspired in this algorithm for optimization . The three levels of wolf hunting are as follows: Tracking, chasing, and approaching the prey. Follow, surround, and annoy the prey until it stops. Prey attack. …”

Section: Proposed Methodsmentioning

confidence: 99%

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A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Li¹,

Zhiwei²,

Cheng³

et al. 2020

Int J Communication

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Summary Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud‐based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud‐based IoT. Since the load balancing issue is one of the NP‐hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well‐known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB‐LB and EBCA‐LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.

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Section: Proposed Methodsmentioning

confidence: 99%

“…The method of hunting such animals has been inspired in this algorithm for optimization. 45,46 The three levels of wolf hunting are as follows:…”

Section: F I G U R E 2 Membership Input Function Of the Assigned Load 43mentioning

confidence: 99%

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Li¹,

Zhiwei²,

Cheng³

et al. 2020

Int J Communication

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“…The payload symbol is calculated prior to the time on air (or packet duration). For a given payload noted PL (in bytes), a spreading factor SF, and a coding rate CR, the number of symbols N PHY used to transmit the payload can be calculated in Equation (7). CRC (cyclic redundancy check) indicates the presence (value 1) or not (value 0) of the CRC field in the physical message and DE indicates if the mechanism to prevent issues about the clock drift of the crystal reference oscillator is used (value 1 for SF12 and SF11, 0 for others) [44,45].…”

Section: Loramentioning

confidence: 99%

Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

Caño

Sánchez-Sutil

2020

Energies

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This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffic, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.

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“…The parent solution is integrated with the neighbor solution to generate the child based on the Cr value, using the following equation: The selection operator was implemented in G-WOA to determine if the target or offspring survived to the following iteration. The selection operator in G-WOA is expressed as in Equations (28) and (29). If every gene value of the generated solution is higher than the mutation value, then the G-WOA will replace the parent solution with the generated one.…”

Section: Crossovermentioning

confidence: 99%

“…Recently, the hybrid approaches of different bio-inspired optimization algorithms [27][28][29] demonstrated competitive performances in different applications of computer science, where two or more algorithms of the following are used as hybrid to optimize the problem in the domain of interest: the genetic algorithm (GA) [30][31][32], social spider optimization [33,34], ant colony optimization (ACO) [35,36], and whale optimization algorithm (WOA) [37,38]. These methods have several merits, including having a small parameter set, simple frameworks, and capability to avoid the shortcoming of being trapped in the local optima.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

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A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems

Cited by 90 publications

References 37 publications

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

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