Showing papers by "Gang Yan published in 2016"

Identifying vital edges in Chinese air route network via memetic algorithm

Abstract: Due to its rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of highest topological importance, for which we provide an extensive explanation from the microscope of view. Our findings also offer new insights to understanding and optimizing other real-world network systems.

Heterogeneous Strategy Particle Swarm Optimization

Abstract: PSO is a widely recognized optimization algorithm inspired by social swarm. In this brief we present a heterogeneous strategy particle swarm optimization (HSPSO), in which a proportion of particles adopt a fully informed strategy to enhance the converging speed while the rest are singly informed to maintain the diversity. Our extensive numerical experiments show that HSPSO algorithm is able to obtain satisfactory solutions, outperforming both PSO and the fully informed PSO. The evolution process is examined from both structural and microscopic points of view. We find that the cooperation between two types of particles can facilitate a good balance between exploration and exploitation, yielding better performance. We demonstrate the applicability of HSPSO on the filter design problem.

The Impact of Population Structure on Particle Swarm Optimization: A Network Science Perspective

Abstract: Particle swarm optimization (PSO) is one of the most important swarm intelligence optimization algorithms due to its ease of implement and outstanding performance. As an information flow system, PSO is influenced by the population structure to a great extent. While previous works considered several classical structure, such as fully-connected and ring structures, here we systematically explore the impact of population structure, including scale-free and small-world networks that have been found in many real-world complex systems. In particular, we examine the influence of average degree, degree distribution and topological randomness of the networks underlying PSO. Our results are not only useful for developing more effective structures to improve the performance of PSO but also helpful in bridging the two fast-growing fields– network science and swarm intelligence.