Abstract: In order to improve the performance of particle swarm optimization algorithm for solving travelling
salesman problem, the shuffled frog leaping algorithm is merged with particle swarm optimization algorithm in the
initial period. Because the initial particles are arbitrary and the distribution is uneven, the particle swarm is
carried out by using the grouping strategy of the shuffled frog leaping algorithm. The suboptimal individuals are
optimized by the improved frog leaping update formula, and new particle swarm is obtained by extracting
individuals at each level, so as to improve the speed of obtaining the optimal individual. In the later period of the
algorithm, the triple crossover strategy and guided mutation operation based on density are introduced to solve the
problem that the particle diversity reduces gradually and the algorithm is easy to fall into local optimum. The
results show that the improved algorithm is effective and superior to other algorithms.

Key words: crossover and mutation, shuffled frog leaping algorithm(SFLA), particle swarm optimization(PSO) algorithm, travelling salesman problem(TSP)