A Novel Quantum Ant Colony Optimization Algorithm

• Published in: Bio-Inspired Computational Intelligence and Applications Vol. 4688; pp. 277 - 286
• Main Authors: Wang, Ling, Niu, Qun, Fei, Minrui
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2007; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Benchmark Function; Genetic Quantum Algorithm; Particle Swarm Optimization Algorithm; Pheromone Trail; Travel Salesperson Problem
• ISBN: 3540747680; 9783540747680
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-74769-7_31

Ant colony optimization (ACO) is a techniqu1e for mainly optimizing the discrete optimization problem. Based on transforming the discrete binary optimization problem as a “best path” problem solved using the ant colony metaphor, a novel quantum ant colony optimization (QACO) algorithm is proposed to tackle it. Different from other ACO algorithms, Q-bit and quantum rotation gate adopted in quantum-inspired evolutionary algorithm (QEA) are introduced into QACO to represent and update the pheromone respectively. Considering the traditional rotation angle updating strategy used in QEA is improper for QACO as their updating mechanisms are different, we propose a new strategy to determine the rotation angle of QACO. The experimental results demonstrate that the proposed QACO is valid and outperforms the discrete binary particle swarm optimization algorithm and QEA in terms of the optimization ability.