In recent years, the field of metaheuristic optimization has seen the development of several innovative algorithms aimed at solving complex optimization problems across various domains. These algorithms are inspired by natural processes, social behaviors, and physical phenomena, offering improved convergence rates and the ability to escape local optima. A sample of such algorithms and their main features are reported in Table 11.1.