Article

Clonal selection with immune dominance and anergy based multiobjective optimization

Authors:

Bin LuAuthors Info & Claims

EMO'05: Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization

Pages 474 - 489

https://doi.org/10.1007/978-3-540-31880-4_33

Published: 09 March 2005 Publication History

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Abstract

Based on the concept of Immunodominance and Antibody Clonal Selection Theory, we propose a new artificial immune system algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA). The influences of main parameters are analyzed empirically. The simulation comparisons among IDCMA, the Random-Weight Genetic Algorithm and the Strength Pareto Evolutionary Algorithm show that when low-dimensional multiobjective problems are concerned, IDCMA has the best performance in metrics such as Spacing and Coverage of Two Sets.

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Liu RLi JSong XYu XJiao L(2018)Simulated annealing-based immunodominance algorithm for multi-objective optimization problemsKnowledge and Information Systems10.1007/s10115-017-1065-x55:1(215-251)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1065-x
Liu RSong XFang LJiao L(2017)An r-dominance-based preference multi-objective optimization for many-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2098-x21:17(5003-5024)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s00500-016-2098-x
Sadollah AEskandar HKim J(2015)Water cycle algorithm for solving constrained multi-objective optimization problemsApplied Soft Computing10.1016/j.asoc.2014.10.04227:C(279-298)Online publication date: 1-Feb-2015
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Clonal selection with immune dominance and anergy based multiobjective optimization

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Information

Published In

EMO'05: Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization

March 2005

910 pages

ISBN:3540249834

Editors:
Carlos A. Coello Coello
Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), México, D.F., México
,
Arturo Hernández Aguirre
Departamento de Computación, Centro de Investigación en Matemáticas (CIMAT), A.P. 402, Guanajuato, México, D.F., Mexico
,
Eckart Zitzler
Computer Engineering and Networks Laboratory, ETH, A.P. 402, Guanajuato, Zurich, D.F., Switzerland

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Springer-Verlag

Berlin, Heidelberg

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Published: 09 March 2005

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Liu RLi JSong XYu XJiao L(2018)Simulated annealing-based immunodominance algorithm for multi-objective optimization problemsKnowledge and Information Systems10.1007/s10115-017-1065-x55:1(215-251)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1065-x
Liu RSong XFang LJiao L(2017)An r-dominance-based preference multi-objective optimization for many-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2098-x21:17(5003-5024)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s00500-016-2098-x
Sadollah AEskandar HKim J(2015)Water cycle algorithm for solving constrained multi-objective optimization problemsApplied Soft Computing10.1016/j.asoc.2014.10.04227:C(279-298)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.asoc.2014.10.042
Yang WZhang CMu B(2014)Towards mashup optimisation with global constraints in the cloudInternational Journal of Grid and Utility Computing10.1504/IJGUC.2014.0653835:4(227-235)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1504/IJGUC.2014.065383
Lin QChen J(2013)A novel micro-population immune multiobjective optimization algorithmComputers and Operations Research10.1016/j.cor.2011.11.01140:6(1590-1601)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1016/j.cor.2011.11.011
Pierrard TCoello Coello C(2012)A multi-objective artificial immune system based on hypervolumeProceedings of the 11th international conference on Artificial Immune Systems10.1007/978-3-642-33757-4_2(14-27)Online publication date: 28-Aug-2012
https://dl.acm.org/doi/10.1007/978-3-642-33757-4_2
Gao JWang J(2010)WBMOAISComputers and Operations Research10.1016/j.cor.2009.03.00937:1(50-61)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.cor.2009.03.009
Das SNatarajan BStevens DKoduru P(2008)Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principleApplied Soft Computing10.1016/j.asoc.2007.05.0128:1(788-797)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.1016/j.asoc.2007.05.012
Campelo FGuimarães FIgarashi H(2007)Overview of artificial immune systems for multi-objective optimizationProceedings of the 4th international conference on Evolutionary multi-criterion optimization10.5555/1762545.1762623(937-951)Online publication date: 5-Mar-2007
https://dl.acm.org/doi/10.5555/1762545.1762623
Shang RMa W(2006)Immune clonal MO algorithm for ZDT problemsProceedings of the Second international conference on Advances in Natural Computation - Volume Part II10.1007/11881223_13(100-109)Online publication date: 24-Sep-2006
https://dl.acm.org/doi/10.1007/11881223_13
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Constrained Multiobjective Optimization Immune Algorithm: Convergence and Application

Immune clonal coevolutionary algorithm for dynamic multiobjective optimization

Immune clonal selection network

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