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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. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow convergence speed and easy to fall into local optimum of SSA, this paper proposed an improved salp swarm algorithm combing information feedback sharing and mayfly search mechanism. Firstly, it used piecewise chaos to initialize the population to make the initial salps more evenly cover the feasible space. It adopted the information sharing mechanism to propose an auxiliary leader strategy, in order to improve the leader position update formula, and enhance the global search ability. It used the evolutionary theory and the idea of positive and negative feedback regulation, selecting better leaders through mutation operation and natural selection, so as to improve the search ability precision. Finally, it put forward the mayfly search mechanism, selected the mating formula of the mayfly algorithm to optimize the iterative formula of the follower position and make the algorithm converge faster in the later stage. Experiments on 12 benchmark functions and 17 CEC test functions prove the comprehensive performance of the improved salp population algorithm, and the ablation experiments verifiy the effectiveness of the improved strategy. The experimental results show that the improved algorithm has obvious advantages in convergence speed and search accuracy. [ABSTRACT FROM AUTHOR]