Ant Colony Optimization Algorithm Enhancement for Better Performance
Mohammed Faisal, Fahad R. Albogamy
Abstract
The scientific field of swarm intelligence is related to swarms existing in natural systems. Numerous systems and Algorithms built on swarm intelligence have appeared addressing optimization problems, including: Firefly Algorithm (FA), Ant System (AS), Particle Swarm Optimization (PSO), Intelligent Water Drops (IWD), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and many others. The foraging behavior of the ant’s system inspired the researchers to generate the ACO and use it to solve optimization problems. In this research, we introduce the ACOStar algorithm to improve the performance of ACO by including the evaluation function of A* algorithm on the transition-probability function of ACO. To demonstrate the success of the proposed algorithm, we applied the suggested algorithm to the shortest path problem. All experiments demonstrate the success and the accuracy of the proposed algorithm.