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Improving Local Search for Minimum Weighted Connected Dominating Set Problem by Inner-Layer Local Search

Authors Bohan Li , Kai Wang, Yiyuan Wang , Shaowei Cai

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Author Details

Bohan Li
  • State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences, Beijing, China
  • School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
Kai Wang
  • School of Computer Science and Information Technology, Northeast Normal University, Changchun, China
Yiyuan Wang
  • School of Computer Science and Information Technology, Northeast Normal University, Changchun, China
  • Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Chnagchun, China
Shaowei Cai
  • State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences, Beijing, China
  • School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

Funding

This work is partially supported by National Key R&D Program of China (2019AAA0105200), Beijing Academy of Artificial Intelligence (BAAI), NSFC Grant 61806050, and Jilin Science and Technology Association QT202005.

Cite AsGet BibTex

Bohan Li, Kai Wang, Yiyuan Wang, and Shaowei Cai. Improving Local Search for Minimum Weighted Connected Dominating Set Problem by Inner-Layer Local Search. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 39:1-39:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.CP.2021.39

Abstract

The minimum weighted connected dominating set (MWCDS) problem is an important variant of connected dominating set problems with wide applications, especially in heterogenous networks and gene regulatory networks. In the paper, we develop a nested local search algorithm called NestedLS for solving MWCDS on classic benchmarks and massive graphs. In this local search framework, we propose two novel ideas to make it effective by utilizing previous search information. First, we design the restart based smoothing mechanism as a diversification method to escape from local optimal. Second, we propose a novel inner-layer local search method to enlarge the candidate removal set, which can be modelled as an optimized version of spanning tree problem. Moreover, inner-layer local search method is a general method for maintaining the connectivity constraint when dealing with massive graphs. Experimental results show that NestedLS outperforms state-of-the-art meta-heuristic algorithms on most instances.

Subject Classification

ACM Subject Classification
  • Theory of computation → Randomized local search
  • Applied computing → Operations research
Keywords
  • Operations Research
  • NP-hard Problem
  • Local Search
  • Weighted Connected Dominating Set Problem

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Supplementary Materials

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