基于衰减因子和动态学习的改进樽海鞘群算法

樽海鞘群算法是一种新型的群智能优化算法.与其他智能优化算法相比,樽海鞘群算法的优化求解策略仍有待改进,以进一步提高该算法的求解精度和寻优效率.本文提出一种基于衰减因子和动态学习的改进樽海鞘群算法,通过在领导者更新阶段添加衰减因子,提高算法的局部开发能力,在跟随者更新阶段引入动态学习策略,提高算法的全局搜索能力.本文对16个测试函数进行实验,将提出的改进算法与其他智能优化算法比较,实验结果表明,本文提出的改进算法在收敛精度和收敛速度方面有较大提升,具有良好的优化性能.

Improved salp swarm algorithm based on reduction factor and dynamic learning

Salp swarm algorithm is one of the most recently proposed swarm intelligent optimization algorithms. Compared with other intelligent optimization algorithms, the optimization strategy of salp swarm algorithm needs to be improved to enhance the convergence accuracy and speed. This paper proposes an improved salp swarm algorithm based on reduction factor and dynamic learning. Firstly, reduction factor is added in the update stage of leader salps, in order to improve the local exploitation ability. Next, dynamic learning strategy is imported in the update stage of follower salps, aiming to improve the global search ability. In this paper, 16 test functions are conducted in the experiment of comparison between the proposed improved algorithm and other intelligent optimization algorithms. The results show that the proposed improved algorithm has a good improvement in convergence accuracy and speed, which has good optimization performance.