具有能级稳定过程的MQHOA优化算法

王鹏; 黄焱

doi:10.11959/j.issn.1000-436x.2016136

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具有能级稳定过程的MQHOA优化算法

学术论文 | 更新时间：2024-06-05

- 具有能级稳定过程的MQHOA优化算法
- MQHOA algorithm with energy level stabilizing process
- 通信学报 2016年37卷第7期页码：79-86
- 作者机构：
  
  1. 西南民族大学计算机科学与技术学院，四川成都 610225
  2. 淮阴师范学院计算机科学与技术学院，江苏淮安 223300
- 作者简介：
  
  [ "王鹏（1975-），男，四川乐山人，西南民族大学教授、博士生导师，主要研究方向为智能算法、大数据、云计算、并行计算。" ]
  [ "黄焱（1982-），男，江苏泗阳人，博士，淮阴师范学院讲师，主要研究方向为智能算法、并行计算。" ]
- 基金信息：
  
  国家自然科学基金资助项目(60702075);模式识别与智能信息处理四川省高校重点实验室开放基金资助项目(MSSB-2015-9)
- DOI：10.11959/j.issn.1000-436x.2016136
  中图分类号： TP301.6
- 网络出版日期：2016-07，
  
  纸质出版日期：2016-07-25
- 稿件说明：
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王鹏, 黄焱. 具有能级稳定过程的MQHOA优化算法[J]. 通信学报, 2016,37(7):79-86.

Peng WANG, Yan HUANG. MQHOA algorithm with energy level stabilizing process[J]. Journal of communications, 2016, 37(7): 79-86.
王鹏, 黄焱. 具有能级稳定过程的MQHOA优化算法[J]. 通信学报, 2016,37(7):79-86. DOI： 10.11959/j.issn.1000-436x.2016136.

Peng WANG, Yan HUANG. MQHOA algorithm with energy level stabilizing process[J]. Journal of communications, 2016, 37(7): 79-86. DOI： 10.11959/j.issn.1000-436x.2016136.

摘要

在量子模型下将优化问题转化为求解约束态的基态波函数问题，通过泰勒近似采用谐振子势阱对目标函数进行逼近，类比量子谐振子的波函数图像提出了一种改进的多尺度量子谐振子优化算法。算法包括3个基本迭代收敛过程：能级稳定过程、能级降低过程和尺度降低过程，算法的收敛过程与物理模型基本吻合。改进算法将主观控制参数减少为1个，同时参照量子模型定义了算法的波函数和零点能。实验结果表明，改进算法的复杂函数优化性能优于多种常见优化算法，对于Ackley、Griewank、Sphere、Sum Squares、Zakharov等高维标准测试函数均能以100%的概率获得全局最优解。

Abstract

An improved multi-scale quantum harmonic oscillator algorithm (MQHOA) with energy level stabilizing process was proposed analogizing to quantum harmonic oscillator's wave function. Inspired by quantum model

the op-timization problem was transformed to finding ground state wave function of bound state. Harmonic oscillator potential well was used to approach objective function under the condition of Taylor approximation. Energy level stabilization

en-ergy level reduction

scale reduction were the basic iterative convergence processes of MQHOA

coinciding with its physical model. Only one subjective control parameter was needed in MQHOA whose wave function and zero-point en-ergy were defined with reference to quantum model. Experimental results show that MQHOA's performance is superior to several other common optimization algorithms. For high dimensional testing functions including Ackley、Griewank、Sphere、Sum Squares、Zakharov

etc

the global optimums can be obtained precisely with 100% probability.

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references

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