Quantum Computing-Based Ant Colony Optimization Algorithm and Performance Analysis

Abstract:

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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.

Info:

Periodical:

Key Engineering Materials (Volumes 460-461)

Edited by:

Yanwen Wu

Pages:

60-65

DOI:

10.4028/www.scientific.net/KEM.460-461.60

Citation:

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

Online since:

January 2011

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Price:

$35.00

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