Salp swarm algorithm based on particle-best

摘要

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.