Study On Co-Evolutionary Ant Colony Algorithm And For Solving Gate Assignment

For air transport,the airport is a necessary place for carrying out transportation activities,providing various foundations and guarantees for all related passenger services,aircraft taking off and landing.As an important resource for the airport,the parking stand is a key factor in achieving fast and safe landings.The allocation of parking places not only from the perspective of passengers and airlines,but also to provide passengers with better services while saving costs for the airlines,but also from the perspective of the airport operations control department,a reasonable,balanced and efficient allocation of limited Parking resources to prevent the adverse impact of unexpected events on the operation of the airport.Therefore,it is of great theoretical significance and application value to carny out research on airport parking allocation modeling and intelligent scheduling algorithms.This dissertation proposes an adaptive co-evolutionary Ant Colony Optimization(SCEACO)algorithm to overcome the deficiencies of the ant colony optimization algorithm such as difficult to determine control parameters and premature convergence.The algorithm firstly updates the pheromone update formula and limits the updating range of the pheromone to achieve the adaptive updating of the pheromone of the ant colony optimization algorithm.Then it draws on the co-evolutionary ideas and symbiotic mechanisms,divides the ant colony into multiple ant colonies with a common search space,and decomposes the multi-objective optimization problem into several sub-optimization problems to achieve population information sharing and co-evolution.Taking the issue of allocation of airport parking places as the research object,based on the consideration of the maximization of airport and airline benefits and the satisfaction of passengers,an optimization model for the allocation of parking places at hub airports was established.Then proposed an airport parking space allocation method based on adaptive co-evolutionary ant colony optimization algorithm.Finally,through the traveling salesman problem,the optimization performance of adaptive co-evolutionary ant colony optimization algorithm is verified.The results show that the algorithm overcomes the problems of difficult to determine control parameters,premature convergence,and so on,has a strong optimization ability and better stability.At the same time,the actual airport flight data was used to verify the effectiveness of the proposed airport parking allocation method.The experimental results show that this method can effectively obtain the allocation result of the parking position.Therefore,this study provides a new method for gate assignment.