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浙江大学学报(工学版)
浙江大学学报(工学版)
电信技术     
求解多目标优化问题的改进布谷鸟搜索算法
杨辉华1,2, 谢谱模1, 张晓凤1, 马巍1, 刘振丙3
1.桂林电子科技大学 广西信息科学实验中心, 广西 桂林 541004; 2.北京邮电大学 自动化学院, 北京100876; 3.桂林电子科技大学 电子工程与自动化学院, 广西 桂林 541004
Improved cuckoo search algorithm for multi-objective optimization problems
YANG Hui-hua1,2 , XIE Pu-mo1, ZHANG Xiao-feng1, MA Wei1, LIU Zhen-bing3
1. Guangxi Experiment Center of Information Science, Guilin University of Electronic Technology, Guilin 541004, China;2. Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China; 3. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
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摘要：

针对求解多目标优化问题, 提出一种改进的多目标布谷鸟搜索算法(IMOCS).相比于标准多目标布谷鸟搜索算法(MOCS),IMOCS在莱维飞行中使用动态自适应的步长控制量,并基于层级和拥挤度距离选择下一次莱维飞行的种群.为了验证算法的有效性,通过在测试实例(SCH,ZDT系列,LZ)计算所求Pareto前沿与真实Pareto前沿的广义距离和所求Pareto前沿的多样性来测试IMOCS的性能.结果表明,与MOCS, NSPSO, NSGA-II比较,IMOCS所求的广义距离更小,即由IMOCS所求Pareto前沿更加接近于真实Pareto前沿,同时IMOCS的Pareto前沿分布更加广泛和均匀,即多样性更好.
Abstract:

In trying to solve multi-objective optimization problems, an improved multi-objective cuckoo search algorithm (IMOCS) was introduced. Compared with the standard multi-objective cuckoo search algorithm (MOCS), the IMOCS had two improvements, used a dynamic adaptive step-size control amount in Lévy flight; chose the next Lévy flight population based on the level and crowding distance. To verify the effectiveness of the IMOCS, test instances (SCH, ZDT series, LZ) were used to evaluate the performance: the generalized distance between the obtained Pareto front and the true Pareto front, and the diversity of the obtained Pareto front. Our results and comparison with the MOCS, NSPSO and NSGA-II showed that the IMOCS obtains a smaller generalized distance, which meant the IMOCSs Pareto front is closer to the true Pareto front, and at the same time its Pareto front distributes broader and more uniform, that diversity is better.
出版日期: 2015-08-01
:  TP 399  
基金资助:

国家自然科学基金资助项目（21365008, 61105004)；广西自然科学基金资助项目（2012GXNSFAA053230, 2013GXNSFBA019279)；广西信息科学实验中心重点资助项目（20130103）.
通讯作者: 刘振丙,男,副教授.     E-mail: 3936924@qq.com
作者简介: 杨辉华（1972—）, 男,教授，博导,从事机器学习、最优化方法研究.E-mail: 13718680586@139.com
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杨辉华, 谢谱模, 张晓凤, 马巍, 刘振丙. 求解多目标优化问题的改进布谷鸟搜索算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2015.08.028.

YANG Hui-hua,XIE Pu-mo, ZHANG Xiao-feng, MA Wei, LIU Zhen-bing. Improved cuckoo search algorithm for multi-objective optimization problems. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2015.08.028.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2015.08.028    或    http://www.zjujournals.com/eng/CN/Y2015/V49/I8/1600

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