An efficient hierarchical parallel genetic algorithm for graph coloring problem

TL;DR: A parallel approach based on Hierarchical Parallel Genetic Algorithms (HPGAs) and a new extension to PGA, that is Genetic Modification (GM) operator designed for solving constraint optimization problems by taking advantage of the properties between variables and their relations are proposed.

Abstract: Graph coloring problems (GCPs) are constraint optimization problems with various applications including scheduling, time tabling, and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. We propose a parallel approach based on Hierarchical Parallel Genetic Algorithms (HPGAs) to solve the GCP. We also propose a new extension to PGA, that is Genetic Modification (GM) operator designed for solving constraint optimization problems by taking advantage of the properties between variables and their relations. Our proposed GM for solving the GCP is based on a novel Variable Ordering Algorithm (VOA). In order to evaluate the performance of our new approach, we have conducted several experiments on GCP instances taken from the well known DIMACS website. The results show that the proposed approach has a high performance in time and quality of the solution returned in solving graph coloring instances taken from DIMACS website. The quality of the solution is measured here by comparing the returned solution with the optimal one.