A Game-Changing Approach to Solve Complex Optimization Problems using Hybrid-QACO

Abstract: Ant colony optimization (ACO) is a widely used metaheuristic algorithm inspired by the foraging behavior of ants. However, ACO has some limitations, such as the possibility of getting stuck in local optima or failing to converge to a global optimal solution. In this paper, we propose a novel hybrid approach that combines ACO with Q- learning, a well-known reinforcement learning algorithm, to improve the behavior of the ant colony. Our hybrid algorithm, called QACO, is designed to enhance the exploitation and exploration phases of ACO by leveraging the learning capabilities of Q-learning. QACO uses Q-values to guide the ants' decision-making process and adaptively adjusts the pheromone trails to avoid premature convergence. To evaluate the effectiveness of our approach, we compared QACO with standard ACO and other state-of-the-art optimization algorithms on several benchmark problems. Experimental results demonstrate that QACO significantly outperforms ACO and other algorithms in terms of convergence speed and solution quality. Moreover, our approach is highly scalable and robust, making it suitable for solving complex optimization problems in various domains. In summary, this paper proposes a novel hybrid approach that effectively enhances the behavior of ACO using Q-learning. The proposed algorithm provides a promising alternative for solving optimization problems and offers valuable insights into the integration of metaheuristic and reinforcement learning techniques.

Keywords: Ant Colony Optimization (ACO), Genetic Algirithms(GA), H-QACO, Q-Learning, Optimization, Metahuristic, TSP, Particle swarm optimization (PSO)