###### Abstract  
Airport gate assignment is a critical issue in airport operations management. However, limited airport parking spaces and rising fuel costs have caused serious issues with gate assignment. In this paper, an effective multiobjective optimisation model for gate assignment is proposed, with the optimisation objectives of minimising real-time flight conflicts, maximising the boarding bridge rate, and minimising aircraft taxiing fuel consumption. An improved tunicate swarm algorithm based on cosine mutation and adaptive grouping (CC-TSA) is proposed to solve the airport gate assignment problem. First, the Halton sequence is used to initialise the agent positions to improve the initial traversal and allocation efficiency of the algorithm. Second, the population as a whole is adaptively divided into dominant and inferior groups based on fitness values. To improve the searchability of the TSA for the dominant group, an arithmetic optimisation strategy based on ideas related to the arithmetic optimisation algorithm (AOA) is proposed. For the inferior group, the global optimal solution is used to guide the update to improve the convergence speed of the algorithm. Finally, the cosine mutation strategy is introduced to perturb the optimal solution and prevent the target from falling into the local extrema as a way to efficiently and reasonably allocate airport gates. The CG-TSA is validated using benchmark test functions, Wilcoxon rank-sum detection, and CEC2017 complex test functions and the results show that the improved algorithm has good optimality-seeking ability and shows high robustness in the multiobjective optimisation problem of airport gate assignment. a 202  
is to optimise the software aspect, allocate gate resources more efficiently, and improve airport gate utilisation efficiency. In comparison to hardware expansion, the latter is more appropriate for solving problems such as insufficient parking spaces, with low investment costs, low risk, and quick results. As a result, studying the optimal configuration of airport gates is extremely practical. The Airport Gate Assignment Problem (AGAP) [2] is a multiobjective optimisation problem that involves allocating resources while taking into account the various stakeholders and the opposing goals for the same stakeholder. Its goal is to distribute a large number of flights among a small number of gates in order to minimise the amount of time passengers must spend walking to their gates. Airport operators strive to decrease resource requirements, resource congestion, disturbances, and delays while also maximising the effectiveness of airport resources. Therefore, this paper provides a review of the literature from two main perspectives: that of the passenger and that of airport operations. According to the first perspective, [PERSON] [3] proposed a 0-1 linear planning method to minimise the passenger travel distance by reducing the distance passengers walk from one gate to another. Although some practical ideas are provided in a dynamic airport environment, the solutions are idealised. [PERSON] et al. [4] proposed a simple tabu search metaheuristic to minimise the time for passengers to change flights. [PERSON] et al. [5] proposed a Pareto-simulated annealing strategy to reduce the number of flights and passengers' walking distances. [PERSON] et al. [6] proposed an improved simulated annealing algorithm based on a cluster search, introducing the neighbourhood construction of the variable neighbourhood search idea to achieve a 0-1 integer planning model for aircraft gate assignment while minimising the number of gates used and taking transit passengers' transfer tensions into account. [PERSON] et al. [7] proposed a hybrid particle swarm algorithm based on the combination of a genetic algorithm and a PSO algorithm to achieve the minimisation of passenger walking distance and parking space idle time equalisation. [PERSON] et al. [8] proposed a new fuzzy swarm optimisation algorithm to reduce total passenger walking distance.