Abstract: To solve the problem that the basic snake optimization algorithm easily falls into local optimization, an improved snake optimization algorithm (SSO) based on dimension selection strategy, selection mating strategy, and re-grouping strategy is proposed. The SSO algorithm introduces the dimension selection strategy in the combat or mating stage of the basic snake optimization algorithm. The random probability is used to select the position update mode of each snake individual in different dimensions, so as to avoid the phenomenon of individual position stagnation in the later stage of iteration. The selection mating strategy is introduced in the combat or mating stage, and a part of individuals with smaller fitness values are selected for combat or mating. The remaining individuals use the exploration stage position update formula for position update to improve the exploration ability of the combat or mating stage. The re-grouping strategy is used, and the individuals are randomly disrupted and re-grouped every ten iterations to increase population diversity and improve the optimization ability of the algorithm. Numerical experiments on 30 standard unconstrained optimization problems show that compared with six comparative algorithms such as the basic snake optimization algorithm SO, the SSO algorithm has stronger optimization ability and is more effective for solving high-dimensional optimization problems. The SSO algorithm is used to optimize the initial weights and thresholds of BP neural networks. Experimental results show that the SSO-BP neural network has better accuracy and stability than other comparative neural networks in classifying wines and predicting abalone age.

Key words: snake optimization algorithm, dimension selection strategy, selection mating strategy, regrouping strategy, numerical experiment