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

作　　者：Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah 

机构地区：[1]Department of Electronics and Electrical Engineering,Shiraz University of Technology,Shiraz,Iran [2]Department of Electrical Engineering,Faculty of Engineering,Jahrom University,Jahrom,Fras,Iran [3]Department of Electrical and Computer Engineering,Tarbiat Modares University,Tehran,Iran [4]Department of Electrical and Computer Engineering,University ofCyprus,Nicosia,Cyprus [5]Centre for Artificial Intelligence Research and Optimisation,Torrens University Australia,Brisbane,QLD 4006,Australia [6]University Research and Innovation Center,Obuda University,1034 Budapest,Hungary [7]Department of Electrical and Computer Engineering,Lebanese American University,Byblos 13-5053,Lebanon [8]Hourani Center for Applied Scientific Research,Al-Ahliyya Amman University,Amman 19328,Jordan [9]MEU Research Unit,Middle East University,Amman 11831,Jordan [10]Applied Science Research Center,Applied Science Private University,Amman 11931,Jordan

出　　处：《Journal of Bionic Engineering》2024年第1期374-408,共35页仿生工程学报（英文版）

摘　　要：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 statistical 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 evaluate 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.

关 键 词：Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA) 

分 类 号：R61[医药卫生—外科学]