A Comprehensive Review of Swarm Intelligence-Based Meta-Heuristic Algorithms

Swarm Intelligence (SI) algorithms, motivated by the group actions of natural systems, offer robust and efficient solutions to complex optimization problems. This review explores several key SI algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Bat Algorithm (BA), and others. Each algorithm uses ideas from nature, like how ants find food and how bees interact with each other, to effectively solve nonlinear and multimodal problems. Comparative analysis focuses on convergence speed, solution quality, robustness, and computational complexity, highlighting the unique strengths and applicability of each algorithm. SI algorithms can be changed and scaled up or down easily, which makes them useful for dynamic and large-scale optimization tasks. This makes them future-proof solutions that can be used in many areas. Future research directions include new algorithms, hybrid approaches, real-world applications, and integration with machine learning techniques, promising continued advances and broader applicability. The development of SI algorithms is driven by their natural inspiration, simplicity, and potential for innovative applications, underscoring their importance in tackling diverse and complex challenges.