J4 ›› 2009, Vol. 47 ›› Issue (4): 759-764.

• 计算机科学 • 上一篇    下一篇

量子统计力学演化算法

黄星焱1, 刘淑芬1, 危明2, 卫楚一1, 张加远1, 陈亮1, 裴升1
  

  1. 1. 吉林大学 计算机科学与技术学院, 长春 130012|2. 武汉大学 软件工程国家重点实验室, 武汉 430072
  • 收稿日期:2008-09-08 出版日期:2009-07-26 发布日期:2009-08-24
  • 通讯作者: 刘淑芬 E-mail:liusf@mail.jlu.edu.cn.

Quantum Statistical Mechanics Evolutionary Algorithm

HUANG Xingyan1, LIU Shu fen1, WEI Ming2, WEI Chuyi1,ZHANG Jiayuan1, CHEN Liang1, PEI Sheng1
  

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;2. State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China
  • Received:2008-09-08 Online:2009-07-26 Published:2009-08-24
  • Contact: LIU Shu fen E-mail:liusf@mail.jlu.edu.cn.

摘要:

将进化理论和量子统计力学理论相结合, 提出一种新的量子统计力学演化算法. 将整个遗传系统作为一个量子统计系统, 并借鉴量子信息论中量子比特的叠加性, 采用量子编码表征染色体, 使系统中的量子能够表示多种线性叠加状态. 算法类比量子统计力学中的相关概念, 定义了量子系统的能量和熵, 并利用量子系统中能量和熵竞争的模式系统地协调进化理论中选择压力和种群多样性间的冲突, 使算法在提高选择压力和维持种群多样性之间保持了适当的平衡, 可以快速的收敛到全局最优解. 实验结果表明, 该算法有较高的执行效率和求解能力.

关键词: 量子系统; 量子力学; 遗传算法

Abstract:

he authors presented a new evolutionary algorithm based on the combination of the evolutionary theory and quantum statistical mechanics. The whole evolutionary system is regarded as a quantum statistical system, where quantum coding is adopted to express chromosomes, and superposition of quantum bits is used to simulate the linear superposition state of the system. Quantum system entropy and statistical energy are defined by analogy with corresponding concepts in quantum statistical mechanics. And the competition between the quantum statistical energy and entropy of the system is used to simulate the conflict between selection pressure and diversity of population, which helps the algorithm to keep a delicate balance between these two issues, and obtain optimal solution rapidly. Numerical experiments show that this new algorithm has a high efficiency and strong ability to get global optimal solution.

Key words: quantum system, quantum mechanics, evolutionary algorithm

中图分类号: 

  • TP18