首页|Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization

Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization

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Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using sta-tistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evalu-ate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.

Nature-inspired algorithmsReal-world and engineering optimizationMathematical modelingGeyser algorithm(GEA)

Mojtaba Ghasemi、Mohsen Zare、Amir Zahedi、Mohammad-Amin Akbari、Seyedali Mirjalili、Laith Abualigah

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Department of Electronics and Electrical Engineering,Shiraz University of Technology,Shiraz,Iran

Department of Electrical Engineering,Faculty of Engineering,Jahrom University,Jahrom,Fras,Iran

Department of Electrical and Computer Engineering,Tarbiat Modares University,Tehran,Iran

Department of Electrical and Computer Engineering,University of Cyprus,Nicosia,Cyprus

Centre for Artificial Intelligence Research and Optimisation,Torrens University Australia,Brisbane,QLD 4006,Australia

University Research and Innovation Center,Obuda University,1034 Budapest,Hungary

Department of Electrical and Computer Engineering,Lebanese American University,Byblos 13-5053,Lebanon

Hourani Center for Applied Scientific Research,Al-Ahliyya Amman University,Amman 19328,Jordan

MEU Research Unit,Middle East University,Amman 11831,Jordan

Applied Science Research Center,Applied Science Private University,Amman 11931,Jordan

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2024

10.1007/s42235-023-00437-8

仿生工程学报(英文版)
吉林大学

仿生工程学报(英文版)

CSTPCDEI
影响因子：0.837
ISSN：1672-6529
年,卷(期)：2024.21(1)
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