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Found 6 papers, 4 papers with code
Incorporating Multi-armed Bandit with Local Search for MaxSAT
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.
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems
LKH-3 is a powerful extension of LKH that can solve many TSP variants.
BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit
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.
Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers
In this work, we observe that most local search (W)PMS solvers usually flip a single variable per iteration.
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem.
Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem
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.
