Grey Wolf Optimizer Driven design space exploration: A novel framework for multi-objective trade-off in architectural synthesis

Siddavaatam, Prathap; Sedaghat, Reza

doi:10.1016/j.swevo.2019.04.006

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Grey wolf optimization (GWO) is a new intelligent algorithm that imitates the hunting behavior of gray wolves in nature to optimize objectives [56] , [57] , [58] . Wolves are classified into 4 classes of α , β , δ and ω according to their fitness degree during hunting.…”

Section: Methodsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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“…Design space exploration procedure during architectural synthesis has two conflicting objectives power performance and the occupied area by resources. GWO is used to tackle this multi-objective problem [202]. Utility coefficients and utility ranking techniques are introduced to develop a unique solution.…”

Section: Multiobjective Versions Of Grey Wolf Optimizationmentioning

confidence: 99%

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.

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“…Grey wolf optimization based on the social order of grey wolves, inspired by the activity of grey wolf hunting prey [14,15], has the characteristics of strong convergence, few parameters, and easy implementation. Grey wolves belong to the social canine family and strictly abide by a hierarchy of social dominance [16]. e first level of social hierarchy: the first wolf of social hierarchy is α wolf, which is mainly responsible for making decisions on predation, habitat, work and rest time, and other activities.…”

Section: The Optimization Of Convolutional Neural Network Based On Im...mentioning

confidence: 99%

An IGWOCNN Deep Method for Medical Education Quality Estimating

Shi

Zheng²

2022

Mathematical Problems in Engineering

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The deep learning and mining ability of big data are used to analyze the shortcomings in the teaching scheme, and the teaching scheme is optimized to improve the teaching ability. The convolution neural network optimized by improved grey wolf optimization is used to train the data so as to improve the efficiency of searching the optimal value of the algorithm and prevent the algorithm from tending to the local optimal value. In order to solve the shortcoming of grey wolf optimization, an improved grey wolf optimization, that is, grey wolf optimization with variable convergence factor, is used to optimize the convolution neural network. The grey wolf optimization with variable convergence factor is to balance the global search ability and local search ability of the algorithm. The testing results show that the quality estimating accuracy of convolutional neural networks optimized by improved grey wolf optimization is 100%, the quality estimating accuracy of convolutional neural networks optimized by grey wolf optimization is 93.33%, and the quality estimating accuracy of classical convolutional neural networks is 86.67%. We can conclude that the medical education quality estimating ability of convolutional neural network optimized by improved grey wolf optimization is the best among convolutional neural networks optimized by improved grey wolf optimization and classical convolutional neural networks.

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Grey Wolf Optimizer Driven design space exploration: A novel framework for multi-objective trade-off in architectural synthesis

Cited by 8 publications

References 23 publications

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

An IGWOCNN Deep Method for Medical Education Quality Estimating

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