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

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.