[1]汤可宗,杨静宇,高尚,等.一种改进的求解多目标优化问题的进化算法[J].南京理工大学学报(自然科学版),2010,(04):464-469.
 TANG Ke-zong,YANG Jing-yu,GAO Shang,et al.Improved Evolutionary Algorithm for Multi-objective Optimization Problems[J].Journal of Nanjing University of Science and Technology,2010,(04):464-469.
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一种改进的求解多目标优化问题的进化算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2010年04期
页码:
464-469
栏目:
出版日期:
2010-08-31

文章信息/Info

Title:
Improved Evolutionary Algorithm for Multi-objective Optimization Problems
作者:
汤可宗;杨静宇;高尚;郑宇杰;
南京理工大学计算机科学与技术学院
Author(s):
TANG Ke-zong12YANG Jing-yu1GAO Shang12ZHENG Yu-jie1
1.School of Computer Science and Technology,NUST,Nanjing 210094,China;2.Key Laboratory of CAD&CG,Zhejiang University,Hangzhou 310027,China
关键词:
多目标优化 进化算法 Pareto最优解 近邻函数
Keywords:
multi-objective optimization evolutionary algorithms Pareto optimal solutions neighborhood function
分类号:
TP301.6
摘要:
针对多目标优化问题,传统进化算法维护种群多样性的方法主要依赖于共享函数,但其小生境半径难以进行有效地设置。该文提出一种改进的求解多目标优化问题的进化算法,新算法引入了近邻函数准则(NFC),将其用于选择过程,可以从种群中选择出较好的个体,并确保种群的多样性。此外,新算法中融入了一种基于近邻函数准则的Pareto候选集的维护方法,利用这种方法可以有效地维护候选解集中个体的多样性。对所提出的算法,从时间和空间复杂度进行了理论分析。对一组典型优化问题的测试表明:该文提出的算法具有较高的搜索性能,解集分布的多样性与收敛性均较理想。
Abstract:
In multi-objective optimization problems,traditional mechanisms of ensuring diversity in a population rely on sharing function.However,the main problem with sharing is that it requires the specification of a sharing parameter.This paper proposes an improved evolutionary algorithm for multi-objective optimization problems and introduces the neighborhood function criterion(NFC) which is applied to selection process.By using this criterion,good individuals can be distinguished from the population and ensure the diversity of the population.On the other hand,the preservation method for Pareto candidate solution set based on NFC is incorporated into the proposed algorithm.This method can maintain the diversity of Pareto candidate set effectively.The complexity of time and space in the proposed algorithm is analysed.For a set of benchmark problems,the experimental results show that the proposed algorithm can search more effectively and provide good performance both in convergence and in diversity of solutions.

参考文献/References:

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[ 2] H orn J, Na fp lio tis N, Go ldberg D E. A n iched Pa re to genetic algorithm for m ultiob jec tive optim ization [ A ]. Proc o f the 1st IEEE Con f on Evo lutionary Com putation[ C] . Piscataway: IEEEW o rld Congress on Compu tational Computation, 1994: 82- 87.
[ 3] Fonseca C M, Flem ing P J. Mu ltiobjective optim ization and mu ltiple constra int handling w ith evo lutionary a lgo rithm s?? Pa rt I: An unified form ulation [ J]. IEEE T rans on Sy stem s, M an, and Cybernetics, 1998, 28( 1): 26- 37.
[ 4] Zitzler E, Thiele L. Mu ltiobjectiv e evo lutionary a lgorithm s: A com para tive case study and the strength Pare to approach[ J] . IEEE Transactions on Evo lutionary Com puta tion, 1999, 3( 9): 257- 271.
[ 5] Deb K, Agrawa l S, Pratap A, e t a.l A fast and e litist m ultiob jective genetic algor ithm: NSGA - II[ J]. IEEE T ransactions on Evo lutionary Computation, 2002, 6( 2): 182- 197.
[ 6] G il C, M?? rquezA, Banos R, et a.l A hybrid m ethod for solvingm ult-i objective g lobal optmi iza tion problems[ J]. Journal o fG loba lOptmi ization, 2007, 38( 2): 265- 281.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 60472060); 浙江大学CAD&CG国家重点实验室开放课题基金资助项目( ( A0704) ) 作者简介: 汤可宗( 1978- ), 男, 博士生, 主要研究方: 智能信息处理、图像处理与模式识别, E-mail: tangkezong@126. com; 通讯作者: 杨静宇( 1941- ), 男, 教授, 博士生导师, 主要研究方向: 模式识别理论与应用、计算机视觉、智能机器人。
更新日期/Last Update: 2012-11-02