Multi-Objective Optimization of Transport Processes on Complex Networks

Transport processes are universal in real-world complex networks, such as communication and transportation networks. As the increase of transport demands in these complex networks, the problems of traffic congestion and transport delay become more and more serious, which call for a systematic network transport optimization. However, it is pretty challenging to improve transport capacity and efficiency simultaneously, since they are often contradictory in that improving one degenerates the other. In this paper, we formulate a multi-objective optimization problem including two objectives: maximizing the transport capacity and minimizing the average number of hops. In this problem, we explore the optimal edge weight assignments and the associated routing paths, corresponding to the optimal trade-off between the two objectives. To solve this problem, we provide a multi-objective evolutionary algorithm, namely network centrality guided multi-objective particle swarm optimization (NC-MOPSO). Specifically, within the framework of MOPSO, 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 five state-of-the-art alternatives on several most-used metrics.