a new improved gray wolf optimization algorithm to solve the aircraft landing problem at mashhad shahid hasheminejad international airport

Air traffic management is a sensitive and stressful job with various daily problems and obstacles. the aircraft landing problem is one of the most important issues addressed currently in flight surveillance. this issue has several optimal local points. gradient-based algorithms cannot produce an optimal solution in a reasonable time to solve this problem. meta-heuristic algorithms are used to solve such problems. since landing earlier or later than the scheduled time will lead to higher costs for each aircraft, this article aims at minimizing the time deviation from the originally scheduled landing time of each flight. the gray wolf algorithm is a new meta-heuristic algorithm inspired by wolf behaviour. however, it has a problem with global and local searches. to solve this problem, the fitness of each wolf is assigned a weight and the new member is obtained using those weights. in addition, in order to increase the local and global search capability of the algorithm, if a condition with a probability of 0.3 is met, a random search is performed around the position of the best wolf. otherwise, by setting a condition with a probability of 0.1, a global search is performed as the mutation operator around a selected wolf. this improves the algorithm’s ability to search globally and locally. in order to evaluate this algorithm in solving the problem, its result is compared with the algorithms of particle swarm optimization, firefly and the common gray wolf. the results show a very high performance of this algorithm compared to other similar algorithms.