Constraint-Based Graph Coloring A Hybrid AI And Optimization Perspective

Abstract

Graph coloring can be defined as a classical optimization problem in combinatory with widespread use in application areas in scheduling, allocation of registers, frequency assignment and network optimization. In large and complicated graphs, more traditional graph coloring methods, including greedy algorithms or optimal methods, are frequently unable to trade-off computation speed with solution quality. The hybrid presented in the paper is a combination of constraint-based reasoning methods and artificial intelligence (AI) techniques along with metaheuristic optimization techniques. The leap in using constraint satisfaction problems (CSP) models in combination with evolutionary algorithms and reinforcement learning benefits the approach under consideration by improving the quality of the solution, lowering the managerial session of computation, and increasing the degree of response to dynamic constraints. The experiments involving benchmark graph datasets prove that hybrid method is more advantageous than other traditional ones in terms of chromatic number minimization and optimal scaling of computing. In this analysis, the usefulness of practical importance of hybrid AI-optimization the tool in practical applications is noted, as well as restrictions in parameters tuning and computation costs, and future research perspectives into adaptive and distributed graph coloring solution applications outlined.