An efficient salp swarm algorithm based on scale-free informed followers with self-adaption weight

Meta-heuristic algorithms are often leveraged to solve complicated engineering optimization and scientific problems. Salp swarm algorithm is one of the most useful meta-heuristic algorithms in recent years. To alleviate the slow convergence speed of the salp swarm algorithm, as well as the tendency to fall into local minima, we have proposed an efficient salp swarm algorithm called E-SSA, which combines the effective evolutionary strategies of basic salp swarm algorithm and two efficient mechanisms named self-adaption weight and scale-free network. These two mechanisms have been integrated into the follower evolution process of the algorithm to achieve the balance of exploration and exploitation. The performance of the E-SSA is benchmarked against a suit of CEC’2019 series functions and 23 commonly used international benchmarks. The algorithm is further validated via three engineering application problems. The experimental results indicate that the improved algorithm has clear advantages in optimization performance compared with other existing heuristic algorithms.