Abstract: A hybrid cycle algorithm was proposed aiming at solving the problems that single algorithm cannot realize intelligent assembly sequence planning of complex assembly and the lack of deep human-computer interaction causes poor improvement effects. This algorithm， based on genetic algorithm， uses interference matrix and contact matrix to adjust random assembly sequence and constructs fitness function on the basis of the uniformity of assembly direction and tool. In addition， combining with simulated annealing algorithm， adding annealing operation and using Metropolis criterion， individual sequences were obtained and accepted by crossover and mutation. Meanwhile， by introducing particle swarm optimization， optimal sequence of individuals and groups were selected to cross with the offspring directly. Finally， assembly simulation platform combined with virtual reality were built and the sequence was optimized from the two dimensions of assembly stability and tool operation space. In conclusion， taking automobile rear axle assembly as an example， it is shown that the assembly sequence obtained fits actual production and the method is effective and practical.

Key words: intelligent assembly sequence planning, genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, virtual reality