scholarly journals Solving (Weighted) Partial MaxSAT by Dynamic Local Search for SAT

2018 ◽  
Author(s):  
Zhendong Lei ◽  
Shaowei Cai
Keyword(s):  
Local Search ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Weighting Scheme ◽  
Local Search Algorithm ◽  
Popular Method ◽  
Real World Applications ◽  
Better Than

Partial MaxSAT (PMS) generalizes SAT and MaxSAT by introducing hard clauses and soft clauses. PMS and Weighted PMS (WPMS) have many important real world applications. Local search is one popular method for solving (W)PMS. Recent studies on specialized local search for (W)PMS have led to significant improvements. But such specialized algorithms are complicated with the concepts tailored for hard and soft clauses. In this work, we propose a dynamic local search algorithm, which exploits the structure of (W)PMS by a carefully designed clause weighting scheme. Our solver SATLike adopts a local search framework for SAT and does not need any specialized concept for (W)PMS. Experiments on PMS and WPMS benchmarks from the MaxSAT Evaluations (MSE) 2016 and 2017 show that SATLike significantly outperforms state of the art local search solvers. Also, SATLike significantly narrows the gap between the performance of local search solvers and complete solvers on industrial benchmarks, and performs better than the complete solvers on the MSE2017 benchmarks.

NuDist: An Efficient Local Search Algorithm for (Weighted) Partial MaxSAT

2019 ◽  
Vol 63 (9) ◽  
pp. 1321-1337
Author(s):  
Zhendong Lei ◽  
Shaowei Cai
Keyword(s):  
Local Search ◽  
State Of The Art ◽  
Search Algorithm ◽  
Local Search Algorithm ◽  
Significant Progress ◽  
Weighted Version ◽  
Maximum Satisfiability ◽  
Real World Applications ◽  
Better Than ◽  
Made In

Abstract Maximum satisfiability (MaxSAT) is the optimization version of the satisfiability (SAT). Partial MaxSAT (PMS) generalizes SAT and MaxSAT by introducing hard and soft clauses, while Weighted PMS (WPMS) is the weighted version of PMS where each soft clause has a weight. These two problems have many important real-world applications. Local search is a popular method for solving (W)PMS. Recently, significant progress has been made in this direction by tailoring local search for (W)PMS, and a representative algorithm is the Dist algorithm. In this paper, we propose two ideas to improve Dist, including a clause-weighting scheme and a variable-selection heuristic. The resulting algorithm is called NuDist. Extensive experiments on PMS and WPMS benchmarks from the MaxSAT Evaluations (MSE) 2016 and 2017 show that NuDist significantly outperforms state-of-the-art local search solvers and performs better than state-of-the-art complete solvers including Open-WBO and WPM3 on MSE 2017 benchmarks. Also, empirical analyses confirm the effectiveness of the proposed ideas.

scholarly journals GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

2006 ◽  
Vol 14 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Frédéric Lardeux ◽  
Frédéric Saubion ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Hybrid Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Satisfiability Problem ◽  
Local Search Algorithm ◽  
Overall Performance ◽  
Genetic Local Search ◽  
Search Stage

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

scholarly journals NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover

2013 ◽  
Vol 46 ◽  
pp. 687-716 ◽  
Author(s):  
S. Cai ◽  
K. Su ◽  
C. Luo ◽  
A. Sattar
Keyword(s):  
Local Search ◽  
Heuristic Algorithms ◽  
State Of The Art ◽  
Search Algorithm ◽  
Vertex Cover ◽  
Experimental Studies ◽  
Maximum Clique ◽  
Local Search Algorithm ◽  
Two Stage ◽  
Edge Weighting

The Minimum Vertex Cover (MVC) problem is a prominent NP-hard combinatorial optimization problem of great importance in both theory and application. Local search has proved successful for this problem. However, there are two main drawbacks in state-of-the-art MVC local search algorithms. First, they select a pair of vertices to exchange simultaneously, which is time-consuming. Secondly, although using edge weighting techniques to diversify the search, these algorithms lack mechanisms for decreasing the weights. To address these issues, we propose two new strategies: two-stage exchange and edge weighting with forgetting. The two-stage exchange strategy selects two vertices to exchange separately and performs the exchange in two stages. The strategy of edge weighting with forgetting not only increases weights of uncovered edges, but also decreases some weights for each edge periodically. These two strategies are used in designing a new MVC local search algorithm, which is referred to as NuMVC. We conduct extensive experimental studies on the standard benchmarks, namely DIMACS and BHOSLIB. The experiment comparing NuMVC with state-of-the-art heuristic algorithms show that NuMVC is at least competitive with the nearest competitor namely PLS on the DIMACS benchmark, and clearly dominates all competitors on the BHOSLIB benchmark. Also, experimental results indicate that NuMVC finds an optimal solution much faster than the current best exact algorithm for Maximum Clique on random instances as well as some structured ones. Moreover, we study the effectiveness of the two strategies and the run-time behaviour through experimental analysis.

scholarly journals From Decimation to Local Search and Back: A New Approach to MaxSAT

2017 ◽  
Author(s):  
Shaowei Cai ◽  
Chuan Luo ◽  
Haochen Zhang
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Optimization Problem ◽  
State Of The Art ◽  
Search Algorithm ◽  
Combinatorial Optimization Problem ◽  
Np Hard ◽  
Local Search Algorithm ◽  
Maximum Satisfiability ◽  
New Approach

Maximum Satisfiability (MaxSAT) is an important NP-hard combinatorial optimization problem with many applications and MaxSAT solving has attracted much interest. This work proposes a new incomplete approach to MaxSAT. We propose a novel decimation algorithm for MaxSAT, and then combine it with a local search algorithm. Our approach works by interleaving between the decimation algorithm and the local search algorithm, with useful information passed between them. Experiments show that our solver DeciLS achieves state of the art performance on all unweighted benchmarks from the MaxSAT Evaluation 2016. Moreover, compared to SAT-based MaxSAT solvers which dominate industrial benchmarks for years, it performs better on industrial benchmarks and significantly better on application formulas from SAT Competition. We also extend this approach to (Weighted) Partial MaxSAT, and the resulting solvers significantly improve local search solvers on crafted and industrial benchmarks, and are complementary (better on WPMS crafted benchmarks) to SAT-based solvers.

scholarly journals Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation

2017 ◽  
Author(s):  
Yi Fan ◽  
Nan Li ◽  
Chengqian Li ◽  
Zongjie Ma ◽  
Longin Jan Latecki ◽  
...  
Keyword(s):  
Random Walk ◽  
Local Search ◽  
Real World ◽  
State Of The Art ◽  
Real Data ◽  
Experimental Results ◽  
Data Sets ◽  
Vertex Weight ◽  
Real World Applications ◽  
Clustering Aggregation

The Maximum Vertex Weight Clique (MVWC) problem is NP-hard and also important in real-world applications. In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will restart. In addition, when the local search has no other options except dropping vertices, it will use random walk. Experimental results show that our solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Also it is the unique solver which is comparable with state-of-the-art methods on both BHOSLIB and large crafted graphs. Furthermore we evaluated our solver in clustering aggregation. Experimental results on a number of real data sets demonstrate that our solver outperforms the state-of-the-art for solving the derived MVWC problem and helps improve the final clustering results.

scholarly journals Distributed Pareto Optimization for Subset Selection

2018 ◽  
Author(s):  
Chao Qian ◽  
Guiying Li ◽  
Chao Feng ◽  
Ke Tang
Keyword(s):  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Subset Selection ◽  
Data Sets ◽  
Mapreduce Framework ◽  
Real World Data ◽  
Real World Applications ◽  
Approximation Guarantee ◽  
Better Than

The subset selection problem that selects a few items from a ground set arises in many applications such as maximum coverage, influence maximization, sparse regression, etc. The recently proposed POSS algorithm is a powerful approximation solver for this problem. However, POSS requires centralized access to the full ground set, and thus is impractical for large-scale real-world applications, where the ground set is too large to be stored on one single machine. In this paper, we propose a distributed version of POSS (DPOSS) with a bounded approximation guarantee. DPOSS can be easily implemented in the MapReduce framework. Our extensive experiments using Spark, on various real-world data sets with size ranging from thousands to millions, show that DPOSS can achieve competitive performance compared with the centralized POSS, and is almost always better than the state-of-the-art distributed greedy algorithm RandGreeDi.

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.

Modified differential evolution with local search algorithm for real world optimization

2011 ◽  
Author(s):  
Ankush Mandal ◽  
Aveek Kumar Das ◽  
Prithwijit Mukherjee ◽  
Swagatam Das ◽  
Ponnuthurai Nagaratnam Suganthan
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Real World ◽  
Search Algorithm ◽  
Local Search Algorithm
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