###### Abstract
[24] proposed a TSA based on local search improvements and introduced a local search algorithm (LEO) [25] to improve the TSA's convergence speed and local search efficiency, which they used for global optimisation and image segmentation. [PERSON] et al. [26] used a TSA to implement a local search algorithm to estimate the optimised values of unknown PV cell parameters at standard temperatures. It has been discovered through the study of TSA that using a TSA's global search capability can avoid the problem of falling into local optimal solutions in the process of finding the optimal solution for traditional multiobjective optimisation problems and can keep the diversity of individual solutions. In summary, the decision to set walking distance objectives for passengers has increased in popularity in recent years, driven by the significance of robust gate schedules for airports and airlines and a growing number of robustness-oriented objectives. Few instances have addressed the requirement for airport operators to simultaneously pursue reduced fuel costs and shorter passenger walking distances. Therefore, in this paper, the AGAP is essentially divided into four objectives: minimising real-time flight conflicts, maximising the boarding bridge rate, minimising the fuel used by aircraft during taxiing, and simultaneously addressing the overall goal of increasing the boarding bridge rate and minimising the fuel used by aircraft. By increasing the effectiveness of airport operations, the pressure on airport fuel prices will be reduced. A survey of the literature on airport gate assignment [27] issues revealed that a number of heuristic and metaheuristic methods have been employed to find solutions. Few have, however, suggested multiobjective precise approaches, and some metaheuristics might generate infeasible solutions. Therefore, an improved tunicate swarm algorithm based on cosine mutation and adaptive grouping (CG-TSA) is proposed in this paper. The Halton sequence initialisation algorithm was introduced to allow the TSA to traverse all possible gates within the entire airport during the initial search in order to improve assignment efficiency. A cosine mutation strategy is used for the gate assignment multiobjective problem to prevent the algorithm from being limited to the optimisation performance of one objective at the expense of the solvency of the others and to prevent the single-objective optimisation from falling into the local extrema, thereby improving the robustness of the gate multiobjective problem solution. The proposed CG-TSA generates partially feasible solutions and improves them during the iterative process. The efficiency of the optimisation procedure can thus be improved. The remainder of the paper is organised as follows. A multiobjective optimisation model for gate assignments is developed in Section 2. In Section 3, an improved TSA based on cosine mutation and adaptive grouping is proposed. Section 4 evaluates the performance of CG-TSA using the benchmark test function, the benchmark test function Wilcoxon rank-sum detection, and the CEC2017 test function. Section 5 presents the application of CG-TSA to the gate assignment multiobjective optimisation problem and simulates and analyses the data, and these results are compared with the TSA and GA. The conclusion of this work is provided in Section 6.