Work

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

Year: 2021

Type: article

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.

Source: DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

DOI: 10.4230/lipics.cp.2021.39

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

Institutions: Chinese Academy of Sciences, Institute of Software, University of Chinese Academy of Sciences, State Key Laboratory of Computer Science, Northeast Normal University

Language: English

Topic: Complexity and Algorithms in Graphs

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Award: G1904811900