###### Abstract
[PERSON] [9] proposed mixed integer linear programming (MILP) and the simulated annealing (SA) algorithm to reduce the total walking distance of passengers from the gate to the baggage conveyor as well as the total fuel consumption of the aircraft taxiing from the gate to the baggage conveyor. From the perspective of airport operators, [PERSON] et al. [10] proposed a heuristic and a simulation framework to assist airport gates in the gate assignment for delayed flights. [PERSON] et al. [11] investigated random flight delays to improve static gate assignment performance. [PERSON] et al. [12] used a fixed buffer time between two consecutive flights assigned to the same gate to minimise random flight delays. [PERSON] et al. [13] proposed a recovery planning procedure to maximise the total aircraft assignment preference while minimising the number of constrained expected gate stops. [PERSON] et al. [14] proposed a biogeography-based optimisation algorithm and a new method for estimating conflict probability to solve a multiobjective gate assignment model that minimises the flight conflict probability and the number of flights allocated to the ramp, thereby improving gate assignment robustness. Of the types of methods, mathematical planning methods are popular: these systems assign flights to gates based on specific rules. [PERSON] [15] proposed an airport gate assignment system based on mathematical planning techniques, resulting in an assignment scheme that satisfies both static and dynamic cases at a reasonable computation time. [PERSON] [16] proposed a dynamic planning scheme with the sole goal of maximising the aircraft/gate preference fraction in order to solve the gate assignment problem in linear time relative to the number of flights. [PERSON] et al. [17] developed a network model and proposed a Lagrangian relaxation-based subgradient method algorithm, shortest path algorithm, and Lagrangian heuristic algorithm to help airports allocate gates efficiently. Another class of methods can be broadly categorised as \"heuristic\" methods. [PERSON] et al. [18] proposed a label search-based algorithm to create a unique neighbourhood structure and tabu search strategy for effectively and rationally allocating gates in order to reduce airport operation costs and improve passenger satisfaction. [PERSON] et al. [19] constructed chromosomesusing relative aircraft positions rather than absolute aircraft positions in the gate queue and proposed an infeasibility-free genetic algorithm for multiobjective gaps. [PERSON] et al. [20] proposed a hybrid metaheuristic algorithm based on the shuffle frog leap algorithm (SFLA) and grasshopper optimisation algorithm (GOA) to solve the problem of joint aircraft turnaround time and airport gate assignment. [PERSON] et al. [21] proposed an improved ant colony optimisation algorithm that used a collaborative ant colony strategy and a pheromone update strategy to quickly achieve a reasonable and efficient airport gate assignment. [PERSON] et al. [22] proposed a new tunicate swarm algorithm (TSA) in 2020, which was inspired by tunicate swarm behaviour. Tunicates are the only animals that move through the ocean using fluid jets for propulsion and they survive by using jet propulsion and swarm intelligence. The TSA is easier to implement than other optimisation algorithms, has better average robustness and global search capabilities, and has been successfully applied in a variety of fields. For example, [PERSON] et al. [23] proposed a tent mapping-based tunicate swarm optimisation algorithm that generates initialisation using tent mapping and uses the grey wolf algorithm to generate global search vectors to improve the algorithm's global exploration capability. The Levy flight is also introduced to broaden the search range and provide an optimal economic and environmental dynamic scheduling scheme. [PERSON] et al.