An effective and robust genetic algorithm with hybrid multi-strategy and mechanism for airport gate allocation

Abstract

Gate allocation is a combinatorial scheduling problem with multi-constraint and multi-objective. It is challenging to solve this problem when the size of flights increases continuously. As an adaptive technology with a random search ability, the genetic algorithm (GA) has been widely used for resource scheduling and combinatorial optimization. However, it is prone to slow convergence and falling into local optimal solutions. Therefore, an effective and robust GA based on hybrid multi-strategy of reverse learning, interval probability mutation, and phagocytosis mechanism, called RPIP-GA, is proposed to implement a new airport gate-allocation method. In the RPIP-GA, the population is divided into several subpopulations based on the fitness values of all individuals to prevent population degradation and improve population quality. The reverse learning strategy with elite retention is designed to initialize the population, expand the global search space, improve the quality of the original solution, and increase the population diversity. The phagocytosis mechanism is employed to implement a crossover operation to enhance the convergence rate and local search ability. An interval probability mutation technique is designed to improve the local search ability in the early stage and prevent falling into the local optimum in the later stage. The effectiveness of the RPIP-GA is validated using 45 complex functions selected from the benchmark functions and CEC 2017, 22 real-world engineering problems selected from CEC 2011, and an actual gate allocation problem via comparisons with the GA, PSO, GSA, DNLGSA, WMSDE, PADE, HIRCGA, and other algorithms. The experimental results show that the RPIP-GA can obtain optimal results with improved stability in most cases. The maximum allocation rate of actual airport gates reaches 98%, and the average convergence accuracy increases by 19% compared with that of the GA. The data used in the study is publicly available from the GitHub repository (https://github.com/xiaocangjiu).