Reinforcement learning based local search for grouping problems: A case study on graph coloring
This work addresses computationally difficult combinatorial optimization problems for researchers and practitioners, but it is incremental as it combines existing techniques.
The paper tackled grouping problems, specifically graph coloring, by proposing a reinforcement learning based local search (RLS) approach, achieving competitive performances on DIMACS and COLOR02 benchmarks.
Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms.