scholarly journals Local Search with Dynamic-Threshold Configuration Checking and Incremental Neighborhood Updating for Maximum k-plex Problem

2020 ◽  
Vol 34 (03) ◽  
pp. 2343-2350 ◽  
Author(s):  
Peilin Chen ◽  
Hai Wan ◽  
Shaowei Cai ◽  
Jia Li ◽  
Haicheng Chen
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Promising Result ◽  
Dynamic Threshold ◽  
Local Search Algorithm ◽  
Local Search Algorithms ◽  
Worst Case ◽  
Massive Graphs ◽  
Configuration Checking ◽  
Novel Strategy

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.

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

2018 ◽  
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.

scholarly journals Two-goal Local Search and Inference Rules for Minimum Dominating Set

2020 ◽  
Author(s):  
Shaowei Cai ◽  
Wenying Hou ◽  
Yiyuan Wang ◽  
Chuan Luo ◽  
Qingwei Lin
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Dominating Set ◽  
Inference Rules ◽  
Local Search Algorithm ◽  
Minimum Dominating Set ◽  
Massive Graphs ◽  
Construction Procedure ◽  
Almost All ◽  
Main Ideas

Minimum dominating set (MinDS) is a canonical NP-hard combinatorial optimization problem with applications. For large and hard instances one must resort to heuristic approaches to obtain good solutions within reasonable time. This paper develops an efficient local search algorithm for MinDS, which has two main ideas. The first one is a novel local search framework, while the second is a construction procedure with inference rules. Our algorithm named FastDS is evaluated on 4 standard benchmarks and 3 massive graphs benchmarks. FastDS obtains the best performance for almost all benchmarks, and obtains better solutions than state-of-the-art algorithms on massive graphs.

scholarly journals Reduction and Local Search for Weighted Graph Coloring Problem

2020 ◽  
Vol 34 (03) ◽  
pp. 2433-2441 ◽  
Author(s):  
Yiyuan Wang ◽  
Shaowei Cai ◽  
Shiwei Pan ◽  
Ximing Li ◽  
Monghao Yin
Keyword(s):  
Local Search ◽  
Graph Coloring ◽  
Search Algorithm ◽  
Weighted Graph ◽  
Coloring Problem ◽  
Massive Graphs ◽  
Configuration Checking ◽  
New Variant ◽  
Graph Coloring Problem ◽  
Important Extension

The weighted graph coloring problem (WGCP) is an important extension of the graph coloring problem (GCP) with wide applications. Compared to GCP, where numerous methods have been developed and even massive graphs with millions of vertices can be solved well, fewer works have been done for WGCP, and no solution is available for solving WGCP for massive graphs. This paper explores techniques for solving WGCP, including a lower bound and a reduction rule based on clique sampling, and a local search algorithm based on two selection rules and a new variant of configuration checking. This results in our algorithm RedLS (Reduction plus Local Search). Experiments are conducted to compare RedLS with the state-of-the-art algorithms on massive graphs as well as conventional benchmarks studied in previous works. RedLS exhibits very good performance and robustness. It significantly outperforms previous algorithms on all benchmarks.

scholarly journals NuCDS: An Efficient Local Search Algorithm for Minimum Connected Dominating Set

2020 ◽  
Author(s):  
Bohan Li ◽  
Xindi Zhang ◽  
Shaowei Cai ◽  
Jinkun Lin ◽  
Yiyuan Wang ◽  
...  
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Dominating Set ◽  
Practical Importance ◽  
Connected Dominating Set ◽  
Local Search Algorithm ◽  
Massive Graphs ◽  
Minimum Connected Dominating Set ◽  
Important Extension ◽  
Connectivity Maintenance

The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Despite its practical importance, there are few works on solving MCDS for massive graphs, mainly due to the complexity of maintaining connectivity. In this paper, we propose two novel ideas, and develop a new local search algorithm for MCDS called NuCDS. First, a hybrid dynamic connectivity maintenance method is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and its previous counterpart. Second, we define a new vertex property called \emph{safety} to make the algorithm more considerate when selecting vertices. Experiments show that NuCDS significantly outperforms the state-of-the-art MCDS algorithms on both massive graphs and classic benchmarks.

scholarly journals Efficient Multi-Start Strategies for Local Search Algorithms

2011 ◽  
Vol 41 ◽  
pp. 407-444 ◽  
Author(s):  
A. György ◽  
L. Kocsis
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Target Function ◽  
Search Algorithms ◽  
Local Optimum ◽  
Local Search Algorithm ◽  
Local Search Algorithms ◽  
Unknown Constant ◽  
Lipschitz Optimization

Local search algorithms applied to optimization problems often suffer from getting trapped in a local optimum. The common solution for this deficiency is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resources (in particular, processing time) to the instances depending on their behavior. Hence, a multi-start strategy has to decide (dynamically) when to allocate additional resources to a particular instance and when to start new instances. In this paper we propose multi-start strategies motivated by works on multi-armed bandit problems and Lipschitz optimization with an unknown constant. The strategies continuously estimate the potential performance of each algorithm instance by supposing a convergence rate of the local search algorithm up to an unknown constant, and in every phase allocate resources to those instances that could converge to the optimum for a particular range of the constant. Asymptotic bounds are given on the performance of the strategies. In particular, we prove that at most a quadratic increase in the number of times the target function is evaluated is needed to achieve the performance of a local search algorithm started from the attraction region of the optimum. Experiments are provided using SPSA (Simultaneous Perturbation Stochastic Approximation) and k-means as local search algorithms, and the results indicate that the proposed strategies work well in practice, and, in all cases studied, need only logarithmically more evaluations of the target function as opposed to the theoretically suggested quadratic increase.

scholarly journals Local search algorithms based on benchmark test functions problem

2020 ◽  
Vol 9 (3) ◽  
pp. 529
Author(s):  
Atheer Bassel ◽  
Hussein M. Haglan ◽  
Akeel Sh. Mahmoud
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Optimization Techniques ◽  
Engineering Optimization ◽  
Local Search Algorithm ◽  
Test Functions ◽  
Local Search Algorithms ◽  
Benchmark Test ◽  
Benchmark Test Functions

<p>Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.</p>

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

2017 ◽  
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.

A Chaotic Local Search Algorithm with Adaptive Exchange of Elements for Quadratic Assignment Problems

2014 ◽  
Vol 2 ◽  
pp. 362-365
Author(s):  
Akio Watanabe ◽  
Kaori Kuroda ◽  
Kantaro Fujiwara ◽  
Tohru Ikeguchi
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Quadratic Assignment ◽  
Local Search Algorithm ◽  
Assignment Problems ◽  
Quadratic Assignment Problems
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