Research Area: Metaheuristic Computing
In order to overcome the slow convergence speed, poor global search ability and difficult designing rotation angle of quantum-inspired evolutionary algorithm (QEA), an improved quantum-inspired cooperative co-evolution algorithm based on combining the strategies of cooperative co-evolution, random rotation direction and Hamming adaptive rotation angle, namely MSQCCEA is proposed, which is employed to propose a new airport gate allocation optimization method in this paper. In the proposed MSQCCEA, the cooperative co-evolution strategy is used to improve the global search capability. The random rotation direction strategy is developed to change the quantum evolution direction from one to two in order to avoid local optimal solution and realize the full search of the solution space. A new Hamming adaptive rotation angle strategy is designed to enable individuals to adaptively adjust the rotation angle according to the difference degree between the individual and the target individual, so as to improve the global search ability and convergence speed. A new airport gate allocation optimization method using MSQCCEA is realized to effectively allocate airport gates to the flights. Finally, the knapsack problem and the actual airport gate allocation problem are used to verify the effectiveness of the proposed MSQCCEA and gate allocation optimization method, respectively. The comparison experiment results demonstrate that the proposed MSQCCEA has faster convergence speed and higher convergence accuracy, and the proposed gate allocation optimization method takes on great potential to make decisions for actual airport management.
Keywords:
quantum-inspired evolutionary algorithm
quantum-inspired cooperative co-evolution
random rotation direction
Hamming adaptive rotation angle
knapsack problem
gate allocation optimization
Author(s) Name: Xing Cai, Huimin Zhao, Shifan Shang, Yongquan Zhou, Wu Deng, Huayue Chen, Wuquan Deng
Journal name: Expert Systems with Applications
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.eswa.2021.114629
Volume Information: Volume 171, 1 June 2021, 114629
