关键词: 樽海鞘群算法, 自适应权重, 黄金正弦算法, 混合变异

Abstract:

In order to solve the problems of slow convergence speed, low accuracy and easy to fall into local optimum solution of the standard salp swarm algorithm, an adaptive salp swarm algorithm based on golden sine algorithm and hybrid mutation is proposed. The adaptive weight factor is introduced to strengthen the leading role of elite individuals and improve the convergence speed and accuracy of the basic salp swarm algorithm. The golden sine algorithm is used to optimize the position update mode of the leader and improve the global exploration and local exploitation capacity of the algorithm. The hybrid neighborhood centroid opposition-based learning with cauchy mutation strategy is introduced to disturb the best individual’s position and improve the ability of the algorithm to jump out of local optimum. The optimization performance of the proposed algorithm is evaluated by a sets of simulation experiments on 12 benchmark functions, the experimental results show that the proposed algorithm can significantly improve the optimum speed and accuracy, besides, it has a strong ability to jump out of local optimum.

Key words: salp swarm algorithm, adaptive weight, golden sine algorithm, hybrid mutation