A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement
A complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve th...
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doaj-2ef5319291b644ad841526607a5ef0bc2021-08-10T08:25:11ZengFrontiers Media S.A.Frontiers in Physics2296-424X2021-08-01910.3389/fphy.2021.735998735998A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness EnhancementZhaoxing Li0Zhaoxing Li1Qionghai Liu2Li Chen3College of Information Engineering, Yulin University, Yulin, ChinaSchool of Information Technology, Northwest University, Xi’an, ChinaYellow River, Shaanxi and Mongolia Supervision Bureau, Yulin, ChinaSchool of Information Technology, Northwest University, Xi’an, ChinaA complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve the robustness of a given network. Existing network rewire mechanism improves the robustness of a given network by re-connecting its nodes but keeping the total number of edges or by adding more edges to the given network. In this work we propose a novel yet efficient network rewire mechanism which is based on multiobjective optimization. The proposed rewire mechanism simultaneously optimizes two objective functions, i.e., maximizing network robustness and minimizing edge rewire operations. We further develop a multiobjective discrete partite swarm optimization algorithm to solve the proposed mechanism. Compared to existing network rewire mechanisms, the developed mechanism has two advantages. First, the proposed mechanism does not require specific constraints on the rewire mechanism to the studied network, which makes it more feasible for applications. Second, the proposed mechanism can suggest a set of network rewire choices each of which can improve the robustness of a given network, which makes it be more helpful for decision makings. To validate the effectiveness of the proposed mechanism, we carry out experiments on computer-generated Erdős–Rényi and scale-free networks, as well as real-world complex networks. The results demonstrate that for each tested network, the proposed multiobjective optimization based edge rewire mechanism can recommend a set of edge rewire solutions to improve its robustness.https://www.frontiersin.org/articles/10.3389/fphy.2021.735998/fullcomplex networksnetwork robustnessnetwork rewire mechanismmultiobjective optimizationpartite swarm optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhaoxing Li Zhaoxing Li Qionghai Liu Li Chen |
spellingShingle |
Zhaoxing Li Zhaoxing Li Qionghai Liu Li Chen A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement Frontiers in Physics complex networks network robustness network rewire mechanism multiobjective optimization partite swarm optimization |
author_facet |
Zhaoxing Li Zhaoxing Li Qionghai Liu Li Chen |
author_sort |
Zhaoxing Li |
title |
A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement |
title_short |
A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement |
title_full |
A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement |
title_fullStr |
A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement |
title_full_unstemmed |
A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement |
title_sort |
novel edge rewire mechanism based on multiobjective optimization for network robustness enhancement |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Physics |
issn |
2296-424X |
publishDate |
2021-08-01 |
description |
A complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve the robustness of a given network. Existing network rewire mechanism improves the robustness of a given network by re-connecting its nodes but keeping the total number of edges or by adding more edges to the given network. In this work we propose a novel yet efficient network rewire mechanism which is based on multiobjective optimization. The proposed rewire mechanism simultaneously optimizes two objective functions, i.e., maximizing network robustness and minimizing edge rewire operations. We further develop a multiobjective discrete partite swarm optimization algorithm to solve the proposed mechanism. Compared to existing network rewire mechanisms, the developed mechanism has two advantages. First, the proposed mechanism does not require specific constraints on the rewire mechanism to the studied network, which makes it more feasible for applications. Second, the proposed mechanism can suggest a set of network rewire choices each of which can improve the robustness of a given network, which makes it be more helpful for decision makings. To validate the effectiveness of the proposed mechanism, we carry out experiments on computer-generated Erdős–Rényi and scale-free networks, as well as real-world complex networks. The results demonstrate that for each tested network, the proposed multiobjective optimization based edge rewire mechanism can recommend a set of edge rewire solutions to improve its robustness. |
topic |
complex networks network robustness network rewire mechanism multiobjective optimization partite swarm optimization |
url |
https://www.frontiersin.org/articles/10.3389/fphy.2021.735998/full |
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