Salp swarm algorithm based on particle-best

Aiming at the problem of salp swarm algorithm(SSA) that the global exploration and local exploitation ability is difficult to coordinate and it is easy to fall into local optimum, a new algorithm of salp swarm based on particle-best is proposed. According to the leadership function of the leader, the algorithm divides the iteration into two stages: global exploration and local exploitation. In the global exploration stage, the leader conducts wide-area search centering on the particle-best position. In the local exploitation stage, the leader performs a fine search centering on the global optimal position. The follower is between the particle-best position and the individual current position. The experimental results of 23 general benchmark functions show that the algorithm based on particle-best has a great improvement in convergence speed, accuracy and robustness compared with other algorithms.