A novel local search algorithm with configuration checking and scoring mechanism for the setk-covering problem

The Maximum k-plex Problem is an important combinatorial optimization problem with increasingly wide applications. In this paper, we propose a novel strategy, named Dynamic-threshold Configuration Checking (DCC), to reduce the cycling problem of local search. Due to the complicated neighborhood relations, all the previous local search algorithms for this problem spend a large amount of time in identifying feasible neighbors in each step. To further improve the performance on dense and challenging instances, we propose Double-attributes Incremental Neighborhood Updating (DINU) scheme which reduces the worst-case time complexity per iteration from O(|V|⋅ΔG) to O(k · Δ‾G). Based on DCC strategy and DINU scheme, we develop a local search algorithm named DCCplex. According to the experiment result, DCCplex shows promising result on DIMACS and BHOSLIB benchmark as well as real-world massive graphs. Especially, DCCplex updates the lower bound of the maximum k-plex for most dense and challenging instances.

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A restart local search algorithm for solving maximum set k-covering problem

Neural Computing and Applications ◽

10.1007/s00521-016-2599-7 ◽

2016 ◽

Vol 29 (10) ◽

pp. 755-765 ◽

Cited By ~ 5

Author(s):

Yiyuan Wang ◽

Dantong Ouyang ◽

Minghao Yin ◽

Liming Zhang ◽

Yonggang Zhang

Keyword(s):

Local Search ◽

Search Algorithm ◽

Covering Problem ◽

Local Search Algorithm

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MLQCC: an improved local search algorithm for the set k ‐covering problem

International Transactions in Operational Research ◽

10.1111/itor.12614 ◽

2018 ◽

Vol 26 (3) ◽

pp. 856-887 ◽

Cited By ~ 4

Author(s):

Yiyuan Wang ◽

Chenxi Li ◽

Huanyao Sun ◽

Jiejiang Chen ◽

Minghao Yin

Keyword(s):

Local Search ◽

Search Algorithm ◽

Covering Problem ◽

Local Search Algorithm

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A Fast Local Search Algorithm for Minimum Weight Dominating Set Problem on Massive Graphs

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/210 ◽

2018 ◽

Cited By ~ 3

Author(s):

Yiyuan Wang ◽

Shaowei Cai ◽

Jiejiang Chen ◽

Minghao Yin

Keyword(s):

Local Search ◽

Real World ◽

Search Algorithm ◽

Dominating Set ◽

Minimum Weight ◽

Local Search Algorithm ◽

Massive Graphs ◽

Configuration Checking ◽

Dominating Set Problem ◽

Short Time

The minimum weight dominating set (MWDS) problem is NP-hard and also important in many applications. Recent heuristic MWDS algorithms can hardly solve massive real world graphs effectively. In this paper, we design a fast local search algorithm called FastMWDS for the MWDS problem, which aims to obtain a good solution on massive graphs within a short time. In this novel local search framework, we propose two ideas to make it effective. Firstly, we design a new fast construction procedure with four reduction rules to cut down the size of massive graphs. Secondly, we propose the three-valued two-level configuration checking strategy to improve local search, which is interestingly a variant of configuration checking (CC) with two levels and multiple values. Experiment results on a broad range of massive real world graphs show that FastMWDS finds much better solutions than state of the art MWDS algorithms.

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CCEHC: An Efficient Local Search Algorithm for Weighted Partial Maximum Satisfiability (Extended Abstract)

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/716 ◽

2017 ◽

Cited By ~ 1

Author(s):

Chuan Luo ◽

Shaowei Cai ◽

Kaile Su ◽

Wenxuan Huang

Keyword(s):

Local Search ◽

State Of The Art ◽

Search Algorithm ◽

Stochastic Local Search ◽

Local Search Algorithm ◽

Maximum Satisfiability ◽

Biased Random Walk ◽

Configuration Checking ◽

Significant Generalization ◽

Max Sat

Weighted partial maximum satisfiability (WPMS) is a significant generalization of maximum satisfiability (MAX-SAT), with many important applications. Recently, breakthroughs have been made on stochastic local search (SLS) for weighted MAX-SAT and (unweighted) partial MAX-SAT (PMS). However, the performance of SLS for WPMS lags far behind. In this work, we present a new SLS algorithm named CCEHC for WPMS. CCEHC is mainly based on a heuristic emphasizing hard clauses, which has three components: a variable selection mechanism focusing on configuration checking based only on hard clauses, a weighting scheme for hard clauses, and a biased random walk component. Experiments show that CCEHC significantly outperforms its state-of-the-art SLS competitors. Experiments comparing CCEHC with a state-of-the-art complete solver indicate the effectiveness of CCEHC on a number of application WPMS instances.

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