Air transport involves a long-distance journey; it is the main method of transnational travel, which is also an important connection channel between countries/regions. The aviation network is one of the core national transportation networks, whose importance is self-evident. To improve the management efficiency of the aviation network, this study studies the topological characteristics of the global aviation network in detail. The findings show that the global aviation network is a scale-free heterogeneous network, and the aviation network is tolerant of random faults. However, if key nodes are deliberately attacked, the network structure can easily be destroyed into fragments. To further explore the importance of nodes, combined with the background of airport mergers or unions, the node shrinkage method is improved by weighing network edges with the number of edges and ranking the importance of each node in the aviation network. This study compares the results of the node importance calculation by the node shrinkage method and improved weighted node shrinkage method, respectively. The results show that the ranking results obtained from the weighted node shrinkage method are better than those obtained from the traditional node shrinkage method. To further verify the validity of the weighted node shrinkage method, this study conducts a sensitivity analysis by calculating the weights of nodes and edges with different values. The results imply that the rank changes of node importance in the top 20 global aviation networks are the same. Therefore, it is important to find the key nodes in the aviation network and take corresponding protective measures to protect the stability of the global aviation network and improve the efficiency of the management of the aviation network.
Power line inspections in a microgrid can be modeled as the uncertain capacitated arc routing problem, which is a classic combinatorial optimization problem. As an evolutionary computation method, genetic programming is used as a hyper-heuristic method to automatically evolve routing policies that can make real-time decisions in an uncertain environment. Most existing research on genetic programming hyper-heuristic for the uncertain capacitated arc routing problem only focuses on optimizing the total cost of solutions. As a result, the actual routes directed by the routing policies evolved by genetic programming hyper-heuristic are usually not stable, i.e., the routes have large fluctuations in different uncertain environments. However, for marketing or considering the drivers’ and customers’ perspectives, the routes should not be changed too often or too much. Addressing this problem, this study first proposes a method to estimate the similarity between two routes and then extends it for evaluating the stability of the routes in uncertain environments. A novel genetic programming hyper-heuristic, which considers two objectives, i.e., the solution quality (total cost) and the stability of routes, was designed. Experimental studies demonstrate that the proposed genetic programming is hyper-heuristic with stability in consideration and can obtain more stable solutions than the traditional algorithm, without deteriorating the total cost. The approach provided in this study can be easily extended to solving other combinatorial optimization problems in the microgrid.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.