English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Transport processes are universal in real-world complex networks, such ascommunication and transportation networks. As the increase of the traffic inthese complex networks, problems like traffic congestion and transport delayare becoming more and more serious, which call for a systematic optimization ofthese networks. In this paper, we formulate a multi-objective optimizationproblem (MOP) to deal with the enhancement of network capacity and efficiencysimultaneously, by appropriately adjusting the weights of edges in networks. Tosolve this problem, we provide a multi-objective evolutionary algorithm (MOEA)based on particle swarm optimization (PSO), namely network centrality guidedmulti-objective PSO (NC-MOPSO). Specifically, in the framework of PSO, wepropose a hybrid population initialization mechanism and a local searchstrategy by employing the network centrality theory to enhance the quality ofinitial solutions and strengthen the exploration of the search space,respectively. Simulation experiments performed on network models and realnetworks show that our algorithm has better performance than fourstate-of-the-art alternatives on several most-used metrics.

NC-MOPSO: Network centrality guided multi-objective particle swarm optimization for transport optimization on networks