In this review we examined recent developments in swarm intelligence (SI) algorithms, addressing five fundamental research questions: (1) what types of novel algorithms are being developed, (2) how are existing algorithms being modified, (3) what implementation practices are emerging, (4) which initialization strategies are prevalent, and (5) what major research patterns characterize the field. The analysis revealed significant trends in algorithmic development, including the continued development of hybrid methods combining multiple swarm-based approaches, the integration of adaptive mechanisms, and the expansion of nature-inspired algorithms beyond traditional swarm behaviors. Implementation practices show evolution toward sophisticated solution representations, intelligent initialization strategies, and advanced visualization methods. The review identified a shift from simple random initialization toward more nuanced approaches incorporating domain knowledge and diversity preservation techniques. Research patterns indicate convergence toward hybrid and adaptive methodologies, with increasing emphasis on real-world applications and computational efficiency. This systematic analysis provides insights into the current state of swarm intelligence research and suggests future directions for algorithm development and practical deployment.