Assembly Sequence Planning Based on Particle Swarm Optimization Algorithm for Complex Product

According to the characteristics and demands of assembly sequence planning(ASP) of complex products,the particle swarm optimization(PSO) algorithm,which is used mainly to optimize the spatial continuity,is extended to solve the ASP problem.The algorithm redefines the particle's position,velocity and relevant operations in sequencing space in accordance to the characteristics of solution.To rise above the deficiency that PSO algorithm is easy to fall into local optimization,a new learning mechanism is taken up to improve the optimizability of the algorithm.Based on the interference matrixes,connection matrix and support matrix,the geometrical feasibility,assembly stability and the occurrence of changing the assembly direction are all taken into account as the evaluation indices to form an objective function.The validity and feasibility of the proposed algorithm have been verified via exemplification.