Quantum Computing-Based Ant Colony Optimization Algorithm and Performance Analysis
A novel Ant Colony Optimization algorithm based on Quantum mechanism for Multi-objective traveling salesman problem (MQACO) is proposed. To improve algorithm performance we use self-adaptive operator, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. We analyze the technology to improve algorithm performance. Self-adaptive algorithm has advantages in terms of the adaptability; reliability and the learning ability over traditional organizing algorithm. TSP benchmark instances Chn144 results demonstrate the superiority of MQACO by different parameter in this paper.
X. M. You et al., "Quantum Computing-Based Ant Colony Optimization Algorithm and Performance Analysis", Key Engineering Materials, Vols. 460-461, pp. 60-65, 2011