This paper provides a brief review of metaheuristic algorithms for optimization. Metaheuristics are optimization techniques that can find good solutions for a wide range of optimization problems. They are derivative-free, meaning they do not require derivative calculations, making them more flexible and effective in avoiding local optima. They are also stochastic, starting with random results to avoid premature convergence. Metaheuristics balance exploration and exploitation to find optimal solutions. Metaheuristics are used in various fields such as engineering, science, and computer science. They are particularly useful when explicit equations are not available. The paper discusses different categories of metaheuristics, including evolution-based, swarm intelligence-based, physics-based, human-related, and hybrid algorithms. Each category has its own characteristics and applications. For example, evolution-based algorithms are inspired by natural evolution, while swarm intelligence-based algorithms are inspired by the behavior of social animals. The paper also discusses the challenges associated with metaheuristics, such as the need for parameter tuning and the difficulty in predicting which algorithm will perform best for a particular problem. Additionally, the paper highlights the practical applications of metaheuristics in various domains, including engineering, education, robotics, medical diagnosis, finance, and fraud detection. The paper concludes that while there is a lot of research on metaheuristics, there are still many challenges and opportunities for further research. The authors emphasize the importance of continued investment in metaheuristics to improve their performance and applicability in real-world problems. The paper also highlights the need for a unified method for algorithm analysis and classification to better understand how metaheuristics work.This paper provides a brief review of metaheuristic algorithms for optimization. Metaheuristics are optimization techniques that can find good solutions for a wide range of optimization problems. They are derivative-free, meaning they do not require derivative calculations, making them more flexible and effective in avoiding local optima. They are also stochastic, starting with random results to avoid premature convergence. Metaheuristics balance exploration and exploitation to find optimal solutions. Metaheuristics are used in various fields such as engineering, science, and computer science. They are particularly useful when explicit equations are not available. The paper discusses different categories of metaheuristics, including evolution-based, swarm intelligence-based, physics-based, human-related, and hybrid algorithms. Each category has its own characteristics and applications. For example, evolution-based algorithms are inspired by natural evolution, while swarm intelligence-based algorithms are inspired by the behavior of social animals. The paper also discusses the challenges associated with metaheuristics, such as the need for parameter tuning and the difficulty in predicting which algorithm will perform best for a particular problem. Additionally, the paper highlights the practical applications of metaheuristics in various domains, including engineering, education, robotics, medical diagnosis, finance, and fraud detection. The paper concludes that while there is a lot of research on metaheuristics, there are still many challenges and opportunities for further research. The authors emphasize the importance of continued investment in metaheuristics to improve their performance and applicability in real-world problems. The paper also highlights the need for a unified method for algorithm analysis and classification to better understand how metaheuristics work.