基于支配强度的NSGA2改进算法

doi:10.11896/j.issn.1002-137X.2018.06.033

Abstract

Abstract: NSGA2 algorithm is a simple,efficient and widely used multi-objective evolutionary algorithm.However,when solving high-dimensional and complex nonlinear multi-objective optimization problems in practical engineering field,NSGA2 has some obvious design defects,such as ineffective identification of pseudo non-dominated solutions,low computational efficiency,poor convergence and distribution.In order to remedy the above drawbacks,this paper proposed an improved NSGA2 algorithm based on dominant strength (INSGA2-DS).INSGA2-DS uses the fast dominant strength sorting method to construct non-dominated set,introduces a new crowding distance with considering variance to improve the distribution of solution sets,and adopts the adaptive elitist retention strategy to adjust elitist retention scale in evolutionary process automatically.The experimental results of INSGA2-DS and NSGA2 with standard test functions show that INSGA2-DS algorithm can improve the convergence and distribution of NSGA2 algorithm effectively.

Key words: Dominant strength, Multi-objective evolutionary algorithm, Multi-objective optimization problems, NSGA2

CLC Number:

TP301

LAI Wen-xing, DENG Zhong-min. Improved NSGA2 Algorithm Based on Dominant Strength[J].Computer Science, 2018, 45(6): 187-192.

References

[1]ZITZLER E,DEB K,THIELE L.Comparison of multiobjective evolutionary algorithms:Empirical results[J].Evolutionary Computation,2000,8(2):173-195.
[2]SRINIVAS N,DEB K.Muiltiobjective optimization using non-dominated sorting in genetic algorithms[J].Evolutionary Computation,1994,2(3):221-248.
[3]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[4]KOLLAT J B,REED P M.The value of online adaptive search:a performance comparison of NSGAII,ε-NSGAII and εMOEA[C]//International Conference on Evolutionary Multi-Criterion Optimization.Springer Berlin Heidelberg,2005:386-398.
[5]ZHANG Q,LI H.MOEA/D:A multiobjective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation,2007,11(6):712.
[6]LI H,ZHANG Q.Multiobjective optimization problems with complicated Pareto sets,MOEA/D and NSGA-II[J].IEEE Transactions on Evolutionary Computation,2009,13(2):284-302.
[7]TAN K C,CHIAM S C,MAMUN A A,et al.Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization[J].European Journal of OperationalResearch,2009,197(2):701-713.
[8]ELHOSSINI A,AREIBI S,DONY R.Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization[J].Evolutionary Computation,2010,18(1):127-156.
[9]XIE C W,LI K,LIAO G Y.Improved NSGA2 Algorithm with Differential Evolutionary Local Search[J].Computer Science,2013,40(10):235-238,273.
谢承旺,李凯,廖国勇.一种带差分局部搜索的改进型NSGA2算法[J].计算机科学,2013,40(10):235-238,273.
[10]DEB K,JAIN H.An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach,part I:Solving problems with box constraints[J].IEEE Trans.Evolutionary Computation,2014,18(4):577-601.
[11]SHIM V A,TAN K C,TANG H.Adaptive memetic computing for evolutionary multiobjectiveoptimization[J].IEEE Transactions on Cybernetics,2015,45(4):610-621.
[12]QIU X,XU J X,TAN K C,et al.Adaptive Cross-Generation Differential Evolution Operators for MultiobjectiveOptimization[J].IEEE Transactions on Evolutionary Computation,2016,20(2):232-244.
[13]COELLO C A C,VAN VELDHUIZEN D A,Lamont G B.Evolutionary algorithms for solving multi-objective problems[M].New York:Kluwer Academic,2002.
[14]郑金华.多目标进化算法及其应用[M].北京:科学出版社,2007:109-112.
[15]DEB K.Multi-objective optimization using evolutionary algorithms[M].New York:John Wiley & Sons,2001.
[16]DEB K.Multi-objective genetic algorithms:Problem difficulties and construction of test problems[J].Evolutionary computation,1999,7(3):205-230.
[17]DEB K,THIELE L,LAUMANNS M,et al.Scalable test problems for evolutionary multi objectiveo ptimization[M].London:Springer,2005.
[18]COELLO C A C,PULIDO G T,LECHUGA M S.Handling multiple objectives with particle swarm optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Improved NSGA2 Algorithm Based on Dominant Strength

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 5

Metrics

Comments

Recommended 0

[1]	LIU Xin-ping, GU Chun-hua, LUO Fei, DING Wei-chao. Improved NSGA-II Algorithm Based on Loser Group and Hybrid Coding Strategy [J]. Computer Science, 2019, 46(10): 222-228.
[2]	MA Qing. Multi-objective Evolutionary Algorithm Based Weight Vectors Generation Method of MOEA/D [J]. Computer Science, 2016, 43(Z11): 117-122.
[3]	AI Hao-jun,GONG Su-wen and YUAN Yuan-ming. Research of Cloud Computing Virtual Machine Allocated Strategy on Multi-objective Evolutionary Algorithm [J]. Computer Science, 2014, 41(6): 48-53.
[4]	XIE Cheng-wang,LI Kai and LIAO Guo-yong. Improved NSGA2Algorithm with Differential Evolution Local Search [J]. Computer Science, 2013, 40(10): 235-238.
[5]	. Pareto-based Multi-object Clonal Evolutionary Algorithm [J]. Computer Science, 2012, 39(Z6): 489-492.