no code implementations • 19 Jan 2024 • Jiongzhi Zheng, Zhuo Chen, Chu-min Li, Kun He
In this paper, we propose to transfer the SPB constraint into the clause weighting system of the local search method, leading the algorithm to better solutions.
1 code implementation • 29 Nov 2022 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li, Felip Manyà
In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima.
no code implementations • 18 Aug 2022 • Yanli Liu, Jiming Zhao, Chu-min Li, Hua Jiang, Kun He
Branch-and-Bound (BnB) is the basis of a class of efficient algorithms for MCS, consisting in successively selecting vertices to match and pruning when it is discovered that a solution better than the best solution found so far does not exist.
1 code implementation • 8 Jul 2022 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li
LKH-3 is a powerful extension of LKH that can solve many TSP variants.
no code implementations • 14 Jan 2022 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li, Felip Manya
We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm for these problems, called BandMaxSAT, that applies a multi-armed bandit model to guide the search direction.
no code implementations • 11 Dec 2021 • Mao Luo, Chu-min Li, Xinyun Wu, Shuolin Li, Zhipeng Lü
This approach uses the LRB branching strategy more to solve the instances with a very low vivification ratio.
1 code implementation • 8 Dec 2020 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem.
no code implementations • 22 Jan 2020 • Kun He, Min Zhang, Jianrong Zhou, Yan Jin, Chu-min Li
Inspired by its success in deep learning, we apply the idea of SGD with batch selection of samples to a classic optimization problem in decision version.
no code implementations • 15 May 2019 • Yan-li Liu, Chu-min Li, Hua Jiang, Kun He
Branch-and-bound (BnB) algorithms are widely used to solve combinatorial problems, and the performance crucially depends on its branching heuristic. In this work, we consider a typical problem of maximum common subgraph (MCS), and propose a branching heuristic inspired from reinforcement learning with a goal of reaching a tree leaf as early as possible to greatly reduce the search tree size. Extensive experiments show that our method is beneficial and outperforms current best BnB algorithm for the MCS.
no code implementations • 10 Aug 2018 • Zhen-Xing Xu, Kun He, Chu-min Li
Although Path-Relinking is an effective local search method for many combinatorial optimization problems, its application is not straightforward in solving the MAX-SAT, an optimization variant of the satisfiability problem (SAT) that has many real-world applications and has gained more and more attention in academy and industry.
no code implementations • 29 Jul 2018 • Chu-min Li, Fan Xiao, Mao Luo, Felip Manyà, Zhipeng Lü, Yu Li
Original and learnt clauses in Conflict-Driven Clause Learning (CDCL) SAT solvers often contain redundant literals.