Scheduling Study of Stereo Garage Access Problem Based on Hybrid Cross-Entropy Algorithm

In this paper, an improved cross-entropy hybrid genetic algorithm (CE-GA) is modeled and proposed for the access efficiency problem under multi-machine collaboration in a new air-entry type stereo garage. The algorithm is further optimized by optimizing the initial population and extracting elite samples to ensure that the solution space satisfies the constraints, using the crossover and variation operators of the genetic algorithm to avoid premature convergence, and introducing adaptive factors for task distribution and conflict frequency. Experimental comparisons with the widely used genetic algorithm and its improved algorithms were conducted, and better results were achieved to improve the garage access efficiency.