An improved equilibrium optimizer: dealing with economic emissions problem in power system dispatch

He, Baoling; Lin, Hsiung-Cheng; Meng, Xiao‐Li; Liu, Wei

doi:10.1109/ieacon55029.2022.9951790

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…In order to increase the effectiveness of the ordinary EO, He et al presented the Improved Equilibrium Optimizer (IEO) in 2022 [132]. The IEO combines the chaotic initialization technique with the dynamic sine and cosine components.…”

Section: Dynamic Equilibrium Optimizermentioning

confidence: 99%

“…Further, the experimental result evidently highlights that the proposed NMIEO outperforms other state-of-the-art metaheuristics showcasing its efficacy in feature selection domain. In order to increase the effectiveness of the ordinary EO, He et al presented the Improved Equilibrium Optimizer (IEO) in the year 2022 [132]. The IEO combines the chaotic initialization technique with the dynamic sine and cosine parameters.…”

Section: Chaotic Equilibrium Optimizermentioning

confidence: 99%

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Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rai

Dhal

2023

Arch Computat Methods Eng

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There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.

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Section: Dynamic Equilibrium Optimizermentioning

confidence: 99%

Section: Chaotic Equilibrium Optimizermentioning

confidence: 99%