Salp Swarm

Salp swarm algorithms (SSA) are nature-inspired metaheuristic optimization techniques mimicking the collective foraging behavior of salps. Current research focuses on enhancing SSA's performance through hybridization with other algorithms (e.g., quantum-inspired methods, Harris Hawks Optimization) and adapting it for diverse applications, including dynamic optimization problems, influence maximization in social networks, and cloud computing task scheduling. These improvements aim to overcome limitations like premature convergence and enhance the algorithm's efficiency in solving complex real-world optimization challenges across various engineering and scientific domains. The resulting advancements contribute to the broader field of metaheuristic optimization by providing robust and effective solutions for difficult problems.