Assembly Sequence Intelligent Planning based on Improved Particle Swarm Optimization Algorithm

Combinatorial explosion and limited efficiency when solving complex products with multiple parts are two issues that traditional assembly sequence solution methods frequently run into. To improve the level of assembly sequence planning (ASP), an interference matrix is constructed to convey the fundamental assembly information of a product. Taking the stability of the assembly sequence, the number of assembly direction changes, and the number of assembly tool changes as evaluation indicators, a fitness function is constructed. On the basis of the unique characteristics of the ASP problem, an improved particle swarm optimization (IPSO) approach is devised. Redefining particle positions, velocities, and their update operations, and introducing mutation operators in genetic algorithm (GA) to improve the ability of PSO algorithms to jump out of local optima. Additionally, the algorithm's convergence speed is enhanced by adjusting the value of the inertia weight. Finally, an example is provided to demonstrate the IPSO algorithm's usefulness and efficiency.