Decision Fusion of Combinatorial Optimization Algorithms for Solving the Graph Coloring Problem

The graph coloring problem is a practical method of representing many real world problems including time scheduling, frequency assignment, register allocation, and circuit board testing. The most important fact that makes graph coloring exciting is that finding the minimum number of colors for an arbitrary graph is NP-hard. This project implements combinatorial optimization algorithms (genetic algorithm (GA), simulated annealing algorithm (SA)) and sequential/greedy algorithms (highest order algorithm (HSO) and the sequential algorithm (SQ)) to find a solution for the graph coloring problem. Decision Fusion is then applied on the implemented algorithms to find an optimized solution. Applying decision fusion on the algorithms signifies the importance given to the factors such as the time of execution and availability of processing resources. Extensive testing and evaluations has been performed to determine important parameters and the efficiency of the simulator. The simulator has always produced better results than the SA, GA, SQ and HSO implemented independently.