Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems

We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto dominance-based, scalarizing function-based, and hypervolume-based algorithms. NSGA-II, MOEA/D, SMS-EMOA, and HypE are examined using knapsack pr...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 19; H. 2; S. 264 - 283
Hauptverfasser:	Ishibuchi, Hisao, Akedo, Naoya, Nojima, Yusuke
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.04.2015
Schlagworte:	Approximation algorithms Pareto optimization Search problems Sociology Vectors many-objective problems Evolutionary many-objective optimization evolutionary multiobjective optimization (EMO)
ISSN:	1089-778X, 1941-0026
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Abstract	We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto dominance-based, scalarizing function-based, and hypervolume-based algorithms. NSGA-II, MOEA/D, SMS-EMOA, and HypE are examined using knapsack problems with 2-10 objectives. Our test problems are generated by randomly specifying coefficients (i.e., profits) in objectives. We also generate other test problems by combining two objectives to create a dependent or correlated objective. Experimental results on randomly generated many-objective knapsack problems are consistent with well-known performance deterioration of Pareto dominance-based algorithms. That is, NSGA-II is outperformed by the other algorithms. However, it is also shown that NSGA-II outperforms the other algorithms when objectives are highly correlated. MOEA/D shows totally different search behavior depending on the choice of a scalarizing function and its parameter value. Some MOEA/D variants work very well only on two-objective problems while others work well on many-objective problems with 4-10 objectives. We also obtain other interesting observations such as the performance improvement by similar parent recombination and the necessity of diversity improvement for many-objective knapsack problems.
AbstractList	We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto dominance-based, scalarizing function-based, and hypervolume-based algorithms. NSGA-II, MOEA/D, SMS-EMOA, and HypE are examined using knapsack problems with 2-10 objectives. Our test problems are generated by randomly specifying coefficients (i.e., profits) in objectives. We also generate other test problems by combining two objectives to create a dependent or correlated objective. Experimental results on randomly generated many-objective knapsack problems are consistent with well-known performance deterioration of Pareto dominance-based algorithms. That is, NSGA-II is outperformed by the other algorithms. However, it is also shown that NSGA-II outperforms the other algorithms when objectives are highly correlated. MOEA/D shows totally different search behavior depending on the choice of a scalarizing function and its parameter value. Some MOEA/D variants work very well only on two-objective problems while others work well on many-objective problems with 4-10 objectives. We also obtain other interesting observations such as the performance improvement by similar parent recombination and the necessity of diversity improvement for many-objective knapsack problems.
Author	Akedo, Naoya Ishibuchi, Hisao Nojima, Yusuke
Author_xml	– sequence: 1 givenname: Hisao surname: Ishibuchi fullname: Ishibuchi, Hisao email: hisaoi@cs.osakafu-u.ac.jp organization: Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan – sequence: 2 givenname: Naoya surname: Akedo fullname: Akedo, Naoya email: naoya.akedo@ci.cs.osakafu-u.ac.jp organization: Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan – sequence: 3 givenname: Yusuke surname: Nojima fullname: Nojima, Yusuke email: nojima@cs.osakafu-u.ac.jp organization: Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan
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Cites_doi	10.1109/CEC.2011.5949788 10.1007/978-3-540-24854-5_131 10.1007/978-3-540-70928-2_56 10.1007/978-3-642-37140-0_33 10.1145/2001576.2001666 10.1109/4235.797969 10.1109/TEVC.2013.2262178 10.1016/j.ejor.2007.04.007 10.1109/TEVC.2012.2185702 10.1007/978-3-642-32964-7_14 10.1109/TEVC.2012.2204264 10.1109/TEVC.2013.2281535 10.1109/TEVC.2012.2225064 10.1109/GEFS.2008.4484566 10.1109/TEVC.2003.810752 10.1145/1389095.1389225 10.1162/EVCO_a_00109 10.1007/978-3-540-70928-2_5 10.1109/TEVC.2013.2281525 10.1109/TEVC.2007.892759 10.1109/TEVC.2013.2281533 10.1109/TEVC.2012.2185847 10.1109/TEVC.2009.2033586 10.1109/TEVC.2012.2204403 10.1109/4235.996017 10.1007/978-3-540-89694-4_41 10.1142/5712 10.1109/5326.704576 10.1007/978-3-540-31880-4_19 10.1145/1830761.1830837 10.1007/978-3-642-37140-0_11 10.1109/TEVC.2010.2077298 10.1109/ICSMC.2009.5346628 10.1145/1830483.1830577 10.1109/TEVC.2010.2043365 10.1007/978-3-642-25566-3_7 10.1007/3-540-36970-8_27 10.1109/TEVC.2010.2058119 10.1109/TEVC.2013.2281534 10.1109/TEVC.2010.2098412 10.1007/978-3-540-70928-2_55 10.1109/TEVC.2011.2166159 10.1109/TEVC.2012.2227145 10.1109/TEVC.2010.2064321 10.1109/TEVC.2011.2161872 10.1109/TEVC.2013.2258025 10.1162/EVCO_a_00009 10.1016/j.ejor.2006.08.008 10.1162/evco.2009.17.3.411 10.1109/CEC.2007.4424990
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References	ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 ref53 ref52 ref55 ref54 ref10 zitzler (ref6) 2001 ref17 ref16 ref19 jiang (ref39) 2011 ref18 corne (ref11) 0 khare (ref8) 2003 ref51 ref50 deb (ref2) 2001 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref3 ref5 hughes (ref9) 0 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref38 purshouse (ref7) 0 goldberg (ref1) 1989 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 tan (ref4) 2005 gu (ref40) 2012; 8
References_xml	– ident: ref21 doi: 10.1109/CEC.2011.5949788 – volume: 8 start-page: 3677 year: 2012 ident: ref40 article-title: A multiobjective evolutionary algorithm using dynamic weight design method publication-title: Int J Innov Comput Inf Control – ident: ref49 doi: 10.1007/978-3-540-24854-5_131 – ident: ref43 doi: 10.1007/978-3-540-70928-2_56 – ident: ref38 doi: 10.1007/978-3-642-37140-0_33 – start-page: 222 year: 0 ident: ref9 article-title: Evolutionary many-objective optimization: Many once or one many? publication-title: Proc 2005 IEEE Congr Evol Comput – ident: ref22 doi: 10.1145/2001576.2001666 – ident: ref28 doi: 10.1109/4235.797969 – ident: ref18 doi: 10.1109/TEVC.2013.2262178 – ident: ref47 doi: 10.1016/j.ejor.2007.04.007 – start-page: 773 year: 0 ident: ref11 article-title: Techniques for highly multiobjective optimization: Some non-dominated points are better than others publication-title: GECCO-2007 Proceedings – year: 1989 ident: ref1 publication-title: Genetic Algorithms in Search Optimization and Machine Learning – ident: ref57 doi: 10.1109/TEVC.2012.2185702 – ident: ref27 doi: 10.1007/978-3-642-32964-7_14 – ident: ref34 doi: 10.1109/TEVC.2012.2204264 – ident: ref58 doi: 10.1109/TEVC.2013.2281535 – ident: ref29 doi: 10.1109/TEVC.2012.2225064 – year: 2001 ident: ref2 publication-title: Multi-Objective Optimization Using Evolutionary Algorithms – ident: ref16 doi: 10.1109/GEFS.2008.4484566 – ident: ref55 doi: 10.1109/TEVC.2003.810752 – ident: ref17 doi: 10.1145/1389095.1389225 – ident: ref41 doi: 10.1162/EVCO_a_00109 – ident: ref10 doi: 10.1007/978-3-540-70928-2_5 – ident: ref45 doi: 10.1109/TEVC.2013.2281525 – ident: ref23 doi: 10.1109/TEVC.2007.892759 – ident: ref42 doi: 10.1109/TEVC.2013.2281533 – ident: ref35 doi: 10.1109/TEVC.2012.2185847 – ident: ref31 doi: 10.1109/TEVC.2009.2033586 – ident: ref56 doi: 10.1109/TEVC.2012.2204403 – year: 2001 ident: ref6 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm – ident: ref5 doi: 10.1109/4235.996017 – ident: ref14 doi: 10.1007/978-3-540-89694-4_41 – start-page: 2066 year: 0 ident: ref7 article-title: Evolutionary many-objective optimization: An exploratory analysis publication-title: Proc 2003 IEEE Congr Evol Comput – ident: ref3 doi: 10.1142/5712 – ident: ref54 doi: 10.1109/5326.704576 – ident: ref48 doi: 10.1007/978-3-540-31880-4_19 – ident: ref37 doi: 10.1145/1830761.1830837 – ident: ref46 doi: 10.1007/978-3-642-37140-0_11 – ident: ref44 doi: 10.1109/TEVC.2010.2077298 – ident: ref26 doi: 10.1109/ICSMC.2009.5346628 – ident: ref52 doi: 10.1145/1830483.1830577 – ident: ref50 doi: 10.1109/TEVC.2010.2043365 – ident: ref51 doi: 10.1007/978-3-642-25566-3_7 – start-page: 376 year: 2003 ident: ref8 article-title: Performance scaling of multi-objective evolutionary algorithms publication-title: Evolutionary Multi-Criterion Optimization doi: 10.1007/3-540-36970-8_27 – ident: ref32 doi: 10.1109/TEVC.2010.2058119 – ident: ref59 doi: 10.1109/TEVC.2013.2281534 – year: 2005 ident: ref4 publication-title: Multiobjective Evolutionary Algorithms and Applications – ident: ref33 doi: 10.1109/TEVC.2010.2098412 – ident: ref13 doi: 10.1007/978-3-540-70928-2_55 – ident: ref53 doi: 10.1109/TEVC.2011.2166159 – ident: ref19 doi: 10.1109/TEVC.2012.2227145 – ident: ref20 doi: 10.1109/TEVC.2010.2064321 – start-page: 1260 year: 2011 ident: ref39 article-title: Multiobjective optimization by decomposition with Pareto-adaptive weight vectors publication-title: Proc 7th ICNC – ident: ref36 doi: 10.1109/TEVC.2011.2161872 – ident: ref15 doi: 10.1109/TEVC.2013.2258025 – ident: ref25 doi: 10.1162/EVCO_a_00009 – ident: ref24 doi: 10.1016/j.ejor.2006.08.008 – ident: ref30 doi: 10.1162/evco.2009.17.3.411 – ident: ref12 doi: 10.1109/CEC.2007.4424990
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Snippet	We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto...
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SubjectTerms	Approximation algorithms Pareto optimization Search problems Sociology Vectors
Title	Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems
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