  Transport processes are universal in real-world complex networks, such as
communication and transportation networks. As the increase of the traffic in
these complex networks, problems like traffic congestion and transport delay
are becoming more and more serious, which call for a systematic optimization of
these networks. In this paper, we formulate a multi-objective optimization
problem (MOP) to deal with the enhancement of network capacity and efficiency
simultaneously, by appropriately adjusting the weights of edges in networks. To
solve this problem, we provide a multi-objective evolutionary algorithm (MOEA)
based on particle swarm optimization (PSO), namely network centrality guided
multi-objective PSO (NC-MOPSO). Specifically, in the framework of PSO, we
propose a hybrid population initialization mechanism and a local search
strategy by employing the network centrality theory to enhance the quality of
initial solutions and strengthen the exploration of the search space,
respectively. Simulation experiments performed on network models and real
networks show that our algorithm has better performance than four
state-of-the-art alternatives on several most-used metrics.
