As real-world optimization problems become more complex, the development of sophisticated and robust algorithms has become essential. Consequently, researchers are focusing on advanced optimization methods that efficiently explore the feasible solution space. This involves designing new high-performance algorithms or enhancing existing meta-heuristic methods by integrating advanced evolutionary strategies. Barnacles Mating Optimizer (BMO) is an evolutionary-based meta-heuristic algorithm inspired by the mating behavior of barnacles, incorporating Hardy–Weinberg principles and the sperm-cast mechanism. Introduced in 2020, BMO has attracted significant attention and has been successfully applied across diverse fields due to its simple design, ease of implementation, high flexibility, and efficient convergence. Therefore, this review provides an overview and synthesis of studies employing BMO. It begins with an introduction to BMO, describing its natural inspiration and optimization framework, followed by a discussion of its core operational procedures and theoretical foundations. The paper then presents a comprehensive analysis of recent BMO variants, systematically categorizing them into modified, multi-objective, and hybrid versions. It also examines BMO’s diverse real-world applications, including power and control engineering, classification, image processing, wireless networks, forecasting, and signal processing. In addition, an updated performance evaluation of BMO is provided, comparing its effectiveness against recently published algorithms using the CEC2005 benchmark suite. Key strengths of BMO are highlighted, including its ability to balance exploration and exploitation, adaptability across problem domains, and its potential for hybridization with other optimization algorithms. Finally, potential enhancements and future research directions are outlined, including multi-objective variants, integration with deep learning, and parallel or distributed implementations.

With the rapid advancements in technology and science, optimization theory and algorithms have become increasingly important. A wide range of real-world problems is classified as optimization challenges, and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains, such as machine learning, process control, and engineering design, showcasing their capability to address complex optimization problems. The Stochastic Fractal Search (SFS) algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials. Since its introduction by Hamid Salimi in 2015, SFS has garnered significant attention from researchers and has been applied to diverse optimization problems across multiple disciplines. Its popularity can be attributed to several factors, including its simplicity, practical computational efficiency, ease of implementation, rapid convergence, high effectiveness, and ability to address single- and multi-objective optimization problems, often outperforming other established algorithms. This review paper offers a comprehensive and detailed analysis of the SFS algorithm, covering its standard version, modifications, hybridization, and multi-objective implementations. The paper also examines several SFS applications across diverse domains, including power and energy systems, image processing, machine learning, wireless sensor networks, environmental modeling, economics and finance, and numerous engineering challenges. Furthermore, the paper critically evaluates the SFS algorithm’s performance, benchmarking its effectiveness against recently published meta-heuristic algorithms. In conclusion, the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.