A novel evolution strategy for multiobjective optimization problem

被引:24
作者
Yang, SM [1 ]
Shao, DG
Luo, YJ
机构
[1] Wuhan Univ, State Key Lab Water Resource & Hydropower Engn Sc, Wuhan 430072, Peoples R China
[2] Wuhan Univ, Coll Water Resource & Hydropower, Wuhan 430072, Peoples R China
[3] NE Normal Univ, Coll Urban & Environm Sci, Changchun 130024, Peoples R China
关键词
multiobjective optimization; Pareto optimal; evolution strategy; evolutionary algorithm;
D O I
10.1016/j.amc.2004.12.025
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Recent literatures have suggested that multiobjective evolutionary algorithms (MOEAs) can serve as a more exploratory and effective tool in solving multiobjective optimization problems (MOPs) than traditional optimizers. In order to contain a good approximation of Pareto optimal set with wide diversity associated with the inherent characters and variability of MOPs, this paper proposes a new evolutionary approach-(mu,lambda) multiobjective evolution strategy ((mu,lambda)-MOES). Following the highlight of how to balance proximity and diversity of individuals in exploration and exploitation stages respectively, some cooperative techniques are devised. Firstly, a novel combinatorial exploration operator that develops strong points from Gaussian mutation of proximity exploration and from Cauchy mutation of diversity preservation is elaborately designed. Additionally, we employ a complete nondominance selection so as to ensure maximal pressure for proximity exploitation while a fitness assignment determined by dominance and population diversity information is simultaneous used to ensure maximal diversity preservation. Moreover, a dynamic external archive is introduced to store elitist individuals as well as relatively better individuals and exchange information with the current population when performing archive increase scheme and archive decrease scheme. By graphical presentation and examination of selected performance metrics on three prominent benchmark test functions, (mu,lambda)-MOES is foun to outperform SPEA-11 to some extent in terms of finding a near-optimal, well-extended and uniformly diversified Pareto optimal front. (c) 2005 Elsevier Inc. All rights reserved.
引用
收藏
页码:850 / 873
页数:24
相关论文
共 31 条
[1]  
[Anonymous], 1999, P IEEE C EVOLUTIONAR, DOI DOI 10.1109/CEC.1999.781913
[2]  
Back T., 1996, EVOLUTIONARY ALGORIT
[3]   An Overview of Evolutionary Algorithms for Parameter Optimization [J].
Baeck, Thomas ;
Schwefel, Hans-Paul .
EVOLUTIONARY COMPUTATION, 1993, 1 (01) :1-23
[4]   The balance between proximity and diversity in multiobjective evolutionary algorithms [J].
Bosman, PAN ;
Thierens, D .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2003, 7 (02) :174-188
[5]   Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms [J].
Bosman, PAN ;
Thierens, D .
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2002, 31 (03) :259-289
[6]  
Chambers JM., 1983, WADSWORTH
[7]   REVIEW AND EVALUATION OF MULTIOBJECTIVE PROGRAMMING TECHNIQUES [J].
COHON, JL ;
MARKS, DH .
WATER RESOURCES RESEARCH, 1975, 11 (02) :208-220
[8]  
Cui Xun-Xue, 2001, Chinese Journal of Computers, V24, P979
[9]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[10]   An Overview of Evolutionary Algorithms in Multiobjective Optimization [J].
Fonseca, Carlos M. ;
Fleming, Peter J. .
EVOLUTIONARY COMPUTATION, 1995, 3 (01) :1-16