An Improved Tunicate Swarm Algorithm for Solving the MultiObjective Optimisation Problem of Airport Gate Assignments

Airport gate assignment is a critical issue in airport operations management. However, limited parking spaces and rising fuel costs have caused serious issues with assignment. In this paper, an effective multiobjective optimisation model for proposed, the objectives of minimising real-time flight conflicts, maximising boarding bridge rate, aircraft taxiing consumption. An improved tunicate swarm algorithm based on cosine mutation adaptive grouping (CG-TSA) proposed to solve problem. First, Halton sequence used initialise agent positions improve initial traversal allocation efficiency algorithm. Second, population as whole adaptively divided into dominant inferior groups fitness values. To searchability TSA group, arithmetic strategy ideas related (AOA) proposed. For global optimal solution guide update convergence speed Finally, introduced perturb prevent target from falling local extrema way efficiently reasonably allocate gates. The CG-TSA validated using benchmark test functions, Wilcoxon rank-sum detection, CEC2017 complex functions results show that has good optimality-seeking ability shows high robustness problem