Quantum Computing-based Ant Colony Optimization Algorithm and Performance Analysis

机译：基于量子计算的蚁群优化算法及性能分析

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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 Chnl44 results demonstrate the superiority of MQACO by different parameter in this paper.

机译：提出了一种基于量子机制的蚁群优化算法，求解多目标旅行商问题。为了提高算法性能，我们使用自适应算子，即在前期，我们使用较高的概率来探索更多的搜索空间并收集有用的全局信息。否则，后期我们将使用更高的概率来加速收敛。我们分析该技术以提高算法性能。自适应算法在适应性方面具有优势。可靠性和学习能力优于传统的组织算法。 TSP基准实例Chn144的结果通过不同的参数证明了MQACO的优越性。

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来源
《Components, packaging and manufacturing technology》|2010年|p.60-65|共6页
会议地点 Sanya(CN);Sanya(CN)
作者
YOU Xiao Ming; LIU Sheng; MIAO Xing Wai;
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作者单位

College of Electronic and Electrical Engineering, Shanghai University of Engineering Science,rnShanghai, China;

College of Electronic and Electrical Engineering, Shanghai University of Engineering Science,rnShanghai, China;

College of Electronic and Electrical Engineering, Shanghai University of Engineering Science,rnShanghai, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类一般性问题;
关键词
Quantum computing; Ant system; Self-adaptive strategy; Multi-objective optimization;

机译：量子计算；蚂蚁系统；自适应策略；多目标优化;

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1. Interacted Multiple Ant Colonies Optimization Approach to Enhance the Performance of Ant Colony Optimization Algorithms [J] . Alaa Aljanaby, Ku Ruhana Ku-Mahamud, Norita Md. Norwawi Computer and Information Science . 2010,第1期

机译：交互式多个蚁群优化方法以提高蚁群优化算法的性能
2. Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems [J] . Udayraj, Konica Mulani, Prabal Talukdar, International Journal of Heat and Mass Transfer . 2015,第octa期

机译：反传热问题的蚁群优化，粒子群优化和布谷鸟搜索算法的性能分析和可行性研究
3. Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem [J] . I.D.I.D. Ariyasingha, T.G.I. Fernando Swarm and Evolutionary Computation . 2015,第Null期

机译：旅行商问题多目标蚁群优化算法的性能分析
4. Quantum Computing-based Ant Colony Optimization Algorithm and Performance Analysis [C] . YOU Xiao Ming, LIU Sheng, MIAO Xing Wai International Conference on Components, Packaging and Manufacturing Technology . 2011

机译：基于量子计算的蚁群优化算法和性能分析
5. Design, implementation and performance analysis of the ant colony optimization algorithm for fouting in ad hoc network [D] . Islam, Mohammad Towhidul 2004

机译：Ad hoc网络蚁群优化算法的设计，实现与性能分析。
6. Ant Colony Optimization Algorithm for Continuous Domains Based on Position Distribution Model of Ant Colony Foraging [O] . Liqiang Liu, Yuntao Dai, Jinyu Gao -1

机译：基于蚁群觅食位置分布模型的连续域蚁群优化算法
7. Interacted Multiple Ant Colonies Optimization Approach to Enhance the Performance of Ant Colony Optimization Algorithms [O] . Alaa Aljanaby (corresponding 2015

机译：交互式多蚁群优化方法提高蚁群优化算法的性能

Quantum Computing-based Ant Colony Optimization Algorithm and Performance Analysis

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