关键词: 鹈鹕优化算法, 无迹sigma点变异, 交叉反向, SPM的混沌序列扰动, LSTM神经网络

Abstract: Aiming at the problems of slow searching speed, low accuracy and easy to fall into local optimization in the optimization process of pelican optimization algorithm (POA), a pelican optimization algorithm combining unscented sigma point mutation and cross learning (MPOA) is proposed. Firstly, the random inverse learning strategy is used to generate a random inverse solution for individuals with poor positions in the population, and the unscented sigma points are introduced to mutate the inverse solution, so as to enhance the fine development of the algorithm in the visible range of the search domain and avoid the algorithm falling into local optimum. Secondly, randomness of Levy’s flight is used to improve the crossover and inversion strategy, the individual optimization process is dynamically explored and enriched, the diversity of the algorithm is maintained, and the global search ability of the algorithm is enhanced. Thirdly, the nonlinear convergence factor is introduced to balance the development and exploration ability of the algorithm, and the SPM-based chaotic sequence is utilized to perturb the nonlinear convergence factor in order to increase the diversity of solutions, avoid the algorithm falling into a local optimum at a later stage, and enhance the stability of the algorithm. Experimental simulation is carried out using 12 benchmark test functions, rank sum test and CEC2021 function, and comparative analysis of the optimization searching effect shows that the improved algorithm has stronger global searching ability and faster optimization searching speed. The MPOA algorithm is used to optimize the parameters of long short-term memory network (LSTM) model, and it is applied to the task of climate change prediction. Compared with other LSTM models optimized by six-population intelligent algorithms, the results show that the MPOA-LSTM model has better prediction accuracy.

Key words: pelican optimization algorithm, unscented sigma point mutation, cross and reverse, chaotic sequence disturbance of SPM, LSTM neural network