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Opposition-Based Multi-Tiered Grey Wolf Optimizer for Stochastic Global Optimization Paradigms

Opposition-Based Multi-Tiered Grey Wolf Optimizer for Stochastic Global Optimization Paradigms

Vasudha Bahl, Anoop Bhola
Copyright: © 2022 |Volume: 11 |Issue: 1 |Pages: 26
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781683182627|DOI: 10.4018/ijeoe.295982
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MLA

Bahl, Vasudha, and Anoop Bhola. "Opposition-Based Multi-Tiered Grey Wolf Optimizer for Stochastic Global Optimization Paradigms." IJEOE vol.11, no.1 2022: pp.1-26. http://doi.org/10.4018/ijeoe.295982

APA

Bahl, V. & Bhola, A. (2022). Opposition-Based Multi-Tiered Grey Wolf Optimizer for Stochastic Global Optimization Paradigms. International Journal of Energy Optimization and Engineering (IJEOE), 11(1), 1-26. http://doi.org/10.4018/ijeoe.295982

Chicago

Bahl, Vasudha, and Anoop Bhola. "Opposition-Based Multi-Tiered Grey Wolf Optimizer for Stochastic Global Optimization Paradigms," International Journal of Energy Optimization and Engineering (IJEOE) 11, no.1: 1-26. http://doi.org/10.4018/ijeoe.295982

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Abstract

Researchers are increasingly using algorithms that are influenced by nature because of its ease and versatility, the key components of nature-inspired metaheuristic algorithms are investigated, involving divergence and adoption, investigation and utilization, and dissemination techniques. Grey Wolf Optimizer (GWO), a relatively recent algorithm influenced by the dominance structure and poaching deportment of grey wolves, is a very popular technique for solving realistic mechanical and optical technical challenges. Half of the recurrence in the GWO are committed to the exploration and the other half to exploitation, ignoring the importance of maintaining the correct equilibrium to ensure a precise estimate of the global optimum. To address this flaw, a Multi-tiered GWO (MGWO) is formulated, that further accomplishes an appropriate equivalence among exploration and exploitation, resulting in optimal algorithm efficiency. In comparison to familiar optimization methods, simulations relying on benchmark functions exhibit the efficacy, performance, and stabilization of MGWO.