基于自适应t分布与动态权重的樽海鞘群算法

Salp swarm algorithm based on adaptive t -distribution and dynamic weight

针对樽海鞘群算法寻优精度低、收敛速度慢和易陷入局部最优等缺点,提出一种基于自适应t分布与动态权重的樽海鞘群算法。首先,在领导者位置更新中引入蝴蝶优化算法中的全局搜索阶段公式,以此来增强全局探索能力;然后,在追随者位置更新中引入自适应动态权重因子来加强精英个体的引导作用,从而增强局部开发能力;最后,为了避免算法陷入局部最优,引入自适应t分布变异策略对最优个体进行变异。通过对12个基准测试函数进行求解,根据平均值、标准差、求解成功率、Wilcoxon检验和收敛曲线分析,表明所提出的算法要优于标准樽海鞘群算法,以及参与比较的其他改进樽海鞘群算法和其他群智能算法,说明了其在寻优精度和收敛速度方面都有显著提升,并且具备跳出局部最优的能力。通过将其应用在脱硝入口浓度最低点寻找上,验证了算法的有效性。

Aiming at the shortcoming of salp swarm optimization algorithm such as low accuracy,slow convergence speed and easy to fall into local optimum,this paper proposed an adaptive t-distribution and dynamic weight salp swarm optimization algorithm.Firstly,the leader position update introduced the global search stage formula of butterfly optimization algorithm to enhance the global exploration ability.Secondly,the follower location update introduced an adaptive dynamic weighting factor to strengthen the guiding role of elite individuals,so as to enhance the local development ability.Finally,the adaptive t-distribution mutation strategy mutated the optimal individual in order to avoid the algorithm falling into local optimum.By solving 12 benchmark test functions,and according to comparison results of the mean value,standard deviation,solving success rate,Wilcoxon test and convergence curve,the proposed algorithm was superior to standard salp swarm algorithm,the compared other improved salp swarm algorithm and the compared other swarm intelligence algorithms.The results also show that it has a significant improvement in the optimization accuracy and convergence speed,and has the ability to jump out of local optimum.The experimental results verify the effectiveness of the proposed algorithm by applying it to find the lowest point of denitrification inlet concentration.