基于信息熵的改进PESA算法

王堃; 王琳琳; 刘艳; 张玉华; 吴蒙

doi:10.3969/j.issn.1000-436x.2013.11.005

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基于信息熵的改进PESA算法

学术论文

- 基于信息熵的改进PESA算法
- Improved PESA algorithm based on comentropy
- 通信学报 2013年34卷第11期页码：33-41
- 作者机构：
  
  1. 南京邮电大学宽带无线通信与传感网技术教育部重点实验室，江苏南京 210003
  2. 南京工业大学自动化与电气工程学院，江苏南京 210009
- 作者简介：
  
  [ "王堃（1981-），男，江苏南京人，博士，南京邮电大学副教授、硕士生导师，主要研究方向为无线通信与信息安全、普适计算、物联网与传感网技术、人工智能与数据挖掘等。" ]
  [ "王琳琳（1990-），女，江苏连云港人，南京邮电大学硕士生，主要研究方向为人工智能与数据挖掘。" ]
  [ "刘艳（1982-），女，江苏南京人，南京工业大学讲师，主要研究方向为物联网与传感网技术。" ]
  [ "张玉华（1990-），男，江苏南通人，南京邮电大学硕士生，主要研究方向为人工智能与数据挖掘等。" ]
  [ "吴蒙（1963-），男，上海人，博士，南京邮电大学教授、博士生导师，主要研究方向为无线通信与信息安全等。" ]
- 基金信息：
  
  国家重点基础研究发展规划（“973”计划）基金资助项目(2011CB302903);国家自然科学基金资助项目(61100199);国家自然科学基金资助项目(61100213);国家自然科学基金资助项目(61170276);国家自然科学基金资助项目(61201160);教育部高等学校博士学科点专项科研基金资助项目(20113223120007);江苏省高校自然科学研究基金资助项目(10KJA510035);江苏省高校自然科学研究基金资助项目(12KJD510007);南京邮电大学宽带无线通信与传感网技术教育部重点实验室开放研究基金资助项目(NYKL201107)
- DOI：10.3969/j.issn.1000-436x.2013.11.005
  中图分类号： TP18
- 网络出版日期：2013-11，
  
  纸质出版日期：2013-11-25
- 稿件说明：
移动端阅览
王堃, 王琳琳, 刘艳, 等. 基于信息熵的改进PESA算法[J]. 通信学报, 2013,34(11):33-41.

Kun WANG, Lin-lin WANG, Yan LIU, et al. Improved PESA algorithm based on comentropy[J]. Communication journal, 2013, 34(11): 33-41.
王堃, 王琳琳, 刘艳, 等. 基于信息熵的改进PESA算法[J]. 通信学报, 2013,34(11):33-41. DOI： 10.3969/j.issn.1000-436x.2013.11.005.

Kun WANG, Lin-lin WANG, Yan LIU, et al. Improved PESA algorithm based on comentropy[J]. Communication journal, 2013, 34(11): 33-41. DOI： 10.3969/j.issn.1000-436x.2013.11.005.

摘要

针对PESA算法所需的计算运算量、计算难度及运算时间都随着解集数量的增加而急剧增加的问题，将熵值度量指标引入到PESA算法中，提出了基于信息熵的PESA算法(C-PESA

comentropy-based PESA)。该算法根据信息熵指标在量化度量Pareto解集的分布特性，判断种群进化是否到达成熟阶段，本算法迭代1300次时即到达成熟阶段，从而尽早结束了算法复杂的优化过程，在一定程度上简化了PESA算法的时间复杂度。仿真结果表明，随着进化种群数量的增长，C-PESA算法的计算量只是呈现线性增加，算法的计算时间缩短接近4倍，进化计算效率得到提高。

Abstract

Aiming at the issue that the computational effort the complexity and the running time of PESA algorithm are increasing rapidly with the growth of the solutions set number

a comentropy-based PESA algorithm (C-PESA) by merg-ing the entropy value metric into PESA algorithm was proposed. According to the distributed characteristic of the entropy value metric over the Pareto solution set

the proposed algorithm could determine whether the population has developed to the mature stage

which is reached when the number iterations is 1 300 in C-PESA. Thereby

the optimization process can be finished as soon as possible

and in a certain extent

the time complexity of PESA was simplified. Simula-tion results show that the computational effort of C-PESA increases linearly with the rising number of solutions. Mean-while

the computation time is improved almost four times

and the evolutionary computation efficiency is also enhanced.

关键词

Keywords

references

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