Abstract: In this article,an improved sea-squirt algorithm is proposed for the urban bike-sharing distribution density optimization problem.First,the sharing bicycle distribution density optimization problem is converted into a functional optimization problem,and the objective function of optimization is established with waiting time,time spent,cost and safety cost as evaluation indexes.Secondly,a one-dimensional normal cloud model and a nonlinear decreasing control strategy are introduced to improve the leader search mechanism in the Bottleneck algorithm to enhance the mining ability of local data;an adaptive strategy is introduced to improve the follower search mechanism of the original algorithm to avoid the algorithm falling into the local optimum.Finally,the effectiveness of the proposed optimization algorithm is verified by the standard test function and the simulation of shared bicycle distribution density.The results show that the improved Bottlenose sheath algorithm has better stability and global search capability than the original algorithm,firefly algorithm and artificial bee colony algorithm,and can better optimize the distribution density of shared bicycles and improve the regional utilization rate of shared bicycles,which is a reference value for the development of intelligent transportation.It has certain reference value for the development of intelligent transportation.

Key words: Adaptive strategy, Cloud model, Distribution density optimization, Public bicycle, Salp swarm algorithm