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Surrogate-Assisted Robust Optimization of Large-scale Networks Based on Graph Embedding

Wang, Shuai, Liu, Jing and Jin, Yaochu (2019) Surrogate-Assisted Robust Optimization of Large-scale Networks Based on Graph Embedding IEEE Transactions on Evolutionary Computation.

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Robust optimization of complex networks has attracted much attention in recent years. Although existing methods have been successful in achieving promising results, the computational cost for robust optimization tasks is extremely high, which prevents them from being further applied to large-scale networks. Thus, computationally efficient robust optimization methods are in high demand. This paper proposes a low-cost method for estimating the robustness of networks with the help of graph embedding techniques and surrogate models. An evolutionary algorithm is then developed to find large-scale robust networks by combining the surrogate-assisted low-cost robustness estimator with the time-consuming real robustness measure by means of a model management strategy. Experimental results on different kinds of synthetic and real networks demonstrate the highly competitive search ability of the proposed algorithm. In addition, the algorithm is able to save up to 80% of the computation time for enhancing the robustness of large-scale networks compared with the state-of-the-art methods.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Liu, Jing
Date : 17 October 2019
Funders : The Royal Society
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Complex networks; Robustness; Graph embedding; Surrogate-assisted optimization
Additional Information : This work was supported in part by the General Program of National Natural Science Foundation of China (NSFC) under Grant 61773300, in part by the Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China under Grant 2017JZ017, and in part by the Royal Society under Grant IEC\NSFC\170279.
Depositing User : Diane Maxfield
Date Deposited : 07 Nov 2019 16:00
Last Modified : 07 Nov 2019 16:00

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