Optimization of fan assembly problem based on NEH and NSGA-II algorithms

Fan assembly optimization is a complex problem involving multiple factors and objectives. In this paper, an optimization method based on the improved NEH (Nawaz-Enscore-Ham) greedy algorithm and genetic mutation algorithm is proposed for the two key factors, namely, mass difference and frequency difference, in the fan assembly process. First, the initial solution with certain mass balance is generated by sorting the fan leaves by mass and inserting them one by one into the group with the smallest current mass by the NEH algorithm. Then, the improved genetic mutation algorithm is introduced for optimization, which simulates the natural evolutionary process and continuously evolves the population through selection, crossover and mutation operations to find the optimal solution. During the solution process, more solution space is explored by adjusting the algorithm parameters, such as population size, iteration number and mutation rate, etc., and the optimal solutions satisfying the optimization objectives are finally obtained. These optimal solutions represent the best assembly solutions that can achieve fan mass balance and maximize frequency variance. By optimizing the mass balance and frequency difference of the fan, the stability and smoothness of the machine operation can be improved, the generation of unbalanced moments can be reduced, the effects of vibration and noise can be reduced, and the performance and lifetime of the machine can be enhanced.