Affiliations: [a] Faculty of Business Information, Shanghai Business School, Shanghai, China
| [b]
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China
| [c]
School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
Correspondence:
[*]
Corresponding author. Yingjie Song, School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China. E-mail: [email protected] and Wu Deng, College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China. E-mail: [email protected].
Abstract: In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms.
