Assembly sequence planning for reflector panels based on genetic algorithm and ant Colony optimization

Assembly sequence planning (ASP) can significantly improve assembly accuracy and reduce assembly costs in modern manufacturing industries. Large reflector antennas are difficult to assemble and urgently need ASP. Based on genetic algorithms (GAs) and ant colony optimization (ACO), an approach for ASP of reflector antennas was developed. An accurate simulation of the assembly of the reflectors was required for the evaluation and optimization of the ASP. The initial population was created by ACO and optimized by GA operators to generate an optimal solution. By releasing the information on the optimal solution to the ant search paths of ACO, convergence to a globally optimal solution was accelerated. A model of the finite element simulation was used to simulate the dynamic assembly process of reflectors according to the algorithm results of the proposed approach (GAACO). The proposed approach was tested and compared to GA, and the results indicate that GAACO can improve the quality of the optimal solution, increase the searching efficiency, and reduce the probability of finding a local optimal solution.