Rank-density-based multiobjective genetic algorithm and benchmark test function study
Concerns the use of evolutionary algorithms (EA) in solving multiobjective optimization problems (MOP). We propose the use of a rank-density-based genetic algorithm (RDGA) that synergistically integrates selected features from existing algorithms in a unique way. A new ranking method, automatic accu...
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| Vydáno v: | IEEE transactions on evolutionary computation Ročník 7; číslo 4; s. 325 - 343 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.08.2003
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | Concerns the use of evolutionary algorithms (EA) in solving multiobjective optimization problems (MOP). We propose the use of a rank-density-based genetic algorithm (RDGA) that synergistically integrates selected features from existing algorithms in a unique way. A new ranking method, automatic accumulated ranking strategy, and a "forbidden region" concept are introduced, completed by a revised adaptive cell density evaluation scheme and a rank-density-based fitness assignment technique. In addition, four types of MOP features, such as discontinuous and concave Pareto front, local optimality, high-dimensional decision space and high-dimensional objective space are exploited and the corresponding MOP test functions are designed. By examining the selected performance indicators, RDGA is found to be statistically competitive with four state-of-the-art algorithms in terms of keeping the diversity of the individuals along the tradeoff surface, tending to extend the Pareto front to new areas and finding a well-approximated Pareto optimal front. |
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| AbstractList | Concerns the use of evolutionary algorithms (EA) in solving multiobjective optimization problems (MOP). We propose the use of a rank-density-based genetic algorithm (RDGA) that synergistically integrates selected features from existing algorithms in a unique way. A new ranking method, automatic accumulated ranking strategy, and a "forbidden region" concept are introduced, completed by a revised adaptive cell density evaluation scheme and a rank-density-based fitness assignment technique. In addition, four types of MOP features, such as discontinuous and concave Pareto front, local optimality, high-dimensional decision space and high-dimensional objective space are exploited and the corresponding MOP test functions are designed. By examining the selected performance indicators, RDGA is found to be statistically competitive with four state-of-the-art algorithms in terms of keeping the diversity of the individuals along the tradeoff surface, tending to extend the Pareto front to new areas and finding a well-approximated Pareto optimal front. |
| Author | Haiming Lu Yen, G.G. |
| Author_xml | – sequence: 1 surname: Haiming Lu fullname: Haiming Lu organization: Prediction Corp., Santa Fe, NM, USA – sequence: 2 givenname: G.G. surname: Yen fullname: Yen, G.G. |
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| Cites_doi | 10.1109/CEC.2002.1007013 10.1162/evco.1995.3.1.1 10.1162/evco.1999.7.3.205 10.1109/ICEC.1994.350040 10.1109/ICSMC.1995.537993 10.1109/3468.650319 10.1080/00207729608929211 10.1109/4235.797969 10.1007/3-540-45356-3_83 10.1109/CEC.2000.870296 10.1162/evco.1994.2.3.221 10.1109/4235.585893 10.1162/evco.1996.4.1.1 10.1109/ICEC.1994.350037 10.1145/298151.298382 10.1162/106365600568202 10.1109/CEC.2000.870274 10.1162/106365600568167 10.1007/3-540-61723-X_1022 10.1109/ICEC.1996.542703 |
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| Keywords | Statistical analysis Pareto optimum Genetic algorithm Evolutionary algorithm Benchmark calculation Multiobjective programming Pareto optimality Multiobjective evolutionary algorithm (MOEA) Optimization Search algorithm multiobjective optimization (MO) |
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| References_xml | – volume-title: Multiobjective Evolutionary Algorithm Research: A History and Analysis year: 1998 ident: ref8 – ident: ref34 doi: 10.1109/CEC.2002.1007013 – ident: ref3 doi: 10.1162/evco.1995.3.1.1 – volume-title: Multiobjective Evolutionary Algorithms: Classifications, Analyzes, and New Innovations year: 1999 ident: ref22 – ident: ref31 doi: 10.1162/evco.1999.7.3.205 – ident: ref4 doi: 10.1109/ICEC.1994.350040 – volume-title: Genetic Algorithms in Search, Optimization and Machine Learning. Reading year: 1989 ident: ref14 – start-page: 424 volume-title: Proc. Conf. Genetic and Evolutionary Computation (GECCO-2001) ident: ref28 article-title: Benchmark problem generators and results for the multiobjective degree-constraint minimum spanning tree problem – ident: ref30 doi: 10.1109/ICSMC.1995.537993 – ident: ref9 doi: 10.1109/3468.650319 – volume-title: An Analysis of Behavior of a Class of Genetic Adaptive Systems year: 1975 ident: ref23 – volume: 2 start-page: 255 year: 1996 ident: ref32 article-title: Multicriteria optimization using a genetic algorithm for determining a Pareto front publication-title: Int. J. Syst. Sci. doi: 10.1080/00207729608929211 – ident: ref1 doi: 10.1109/4235.797969 – ident: ref12 doi: 10.1007/3-540-45356-3_83 – ident: ref29 doi: 10.1109/CEC.2000.870296 – ident: ref13 doi: 10.1162/evco.1994.2.3.221 – start-page: 227 volume-title: Proc. Genetic and Evolutionary Computation Conf. ident: ref6 article-title: Evolutionary strategies for a parallel multi-objective genetic algorithm – start-page: 93 volume-title: Proc. 1st Int. Conf. Genetic Algorithms ident: ref15 article-title: Multiple objective optimization with vector evaluated genetic algorithms – ident: ref7 doi: 10.1109/4235.585893 – volume-title: SPEA2: Improving the Strength Pareto Evolutionary Algorithm year: 2001 ident: ref11 – ident: ref20 doi: 10.1162/evco.1996.4.1.1 – start-page: 151 volume-title: Proc. 6th Int. Conf. Genetic Algorithms ident: ref24 article-title: Genetic algorithms, numerical optimization, and constraints – ident: ref18 doi: 10.1109/ICEC.1994.350037 – ident: ref26 doi: 10.1145/298151.298382 – volume: 164 volume-title: Lecture Notes in Economics and Mathematical Systems year: 1979 ident: ref2 article-title: Multiple objective decision making—methods and applications – start-page: 658 volume-title: Proc. 7th Int. Conf. Genetic Algorithms ident: ref16 article-title: A nongenerational genetic algorithm for multiobjective optimization – ident: ref27 doi: 10.1162/106365600568202 – volume-title: Principles and Procedures of Statistics: A Biometrical Approach year: 1997 ident: ref33 – ident: ref21 doi: 10.1109/CEC.2000.870274 – ident: ref10 doi: 10.1162/106365600568167 – start-page: 172 volume-title: Proc. 7th Cong. Evolutionary Computation ident: ref17 article-title: A nongenerational genetic algorithm for multiobjective optimization – start-page: 658 volume-title: Proc. 5th Int. Conf. Genetic Algorithms ident: ref19 article-title: Cellular genetic algorithms – ident: ref25 doi: 10.1007/3-540-61723-X_1022 – ident: ref5 doi: 10.1109/ICEC.1996.542703 |
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| SubjectTerms | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Benchmark testing Computer science; control theory; systems Density Design engineering Distributed computing Evolutionary algorithms Evolutionary computation Exact sciences and technology Genetic algorithms Iron Mathematical analysis Mathematical foundations Mathematical models Mathematics Optimization Pareto optimality Pareto optimization Probability and statistics Ranking Sciences and techniques of general use Statistics Theoretical computing |
| Title | Rank-density-based multiobjective genetic algorithm and benchmark test function study |
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