Optimal Scheduling of Intelligent Building with Photovoltaic Energy Storage System Using Competitive Mechanism Integrated Multi-objective Equilibrium Optimizer Algorithm

Yi, Lingzhi; Li, Guanghua; Chen, Kefu; Liu, Jiankang; Lu, Fan; Gao, Xieyi

doi:10.1007/s13369-022-06831-4

Cited by 2 publications

(1 citation statement)

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“…Further, the experimental result noticeably highlights that the proposed MOEOT outperforms EO and other recent state-of-the-art metaheuristics projecting its efficacy in Radial distribution system. In order to improve the performance of standard EO, Yi et al proposed a new variant of revised EO in 2022 [178] called Multi-Objective Equilibrium Optimizer algorithm integrated Competition Mechanism (CMOEO). This variant incorporates Competition mechanism integrating Differential Evolution by swapping out the constant factors of exploration and exploitation with dynamic factors.…”

Section: Multi-objective Equilibrium Optimizermentioning

confidence: 99%

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: Multi-objective Equilibrium Optimizermentioning

confidence: 99%