Improving the performance of graph coloring algorithms through backtracking

Abstract

Graph coloring is used to identify independent objects in a set and has applications in a wide variety of scientific and engineering problems. Optimal coloring of graphs is an NP-complete problem. Therefore there exist many heuristics that attempt to obtain a near-optimal number of colors. In this paper we introduce a backtracking correction algorithm which dynamically rearranges the colors assigned by a top level heuristic to a more favorable permutation thereby improving the performance of the coloring algorithm. Our results obtained by applying the backtracking heuristic on graphs from molecular dynamics and DNA-electrophoresis show that the backtracking algorithm succeeds in lowering the number of colors by as much as 23%. Variations of backtracking algorithm can be as much as 66% faster than standard correction algorithms, like Culberson's Iterated Greedy method, while producing a comparable number of colors. © 2008 Springer-Verlag Berlin Heidelberg.