ML-MMAS: self-learning ant colony optimization for multi-criteria journey planning

• Main Authors: He, Peilan, Jiang, Guiyuan, Lam, Siew-Kei, Sun, Yidan
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Pheromone Model; Self-Learning
• Online Access: https://hdl.handle.net/10356/163885
• Institution: Nanyang Technological University

Ant Colony Optimization (ACO) algorithms have been widely employed for solving optimization problems. Their ability to find optimal solutions depends heavily on the parameterization of the pheromone trails. However, the pheromone parameterization mechanisms in existing ACO algorithms have two major...