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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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 7; H. 4; S. 325 - 343
Hauptverfasser: Haiming Lu, Yen, G.G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.08.2003
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
Statistical analysis
Pareto optimum
Genetic algorithm
Evolutionary algorithm
Benchmark calculation
Multiobjective programming
Pareto optimality
Multiobjective evolutionary algorithm (MOEA)
Optimization
Search algorithm
multiobjective optimization (MO)
ISSN:1089-778X, 1941-0026
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Beschreibung
Zusammenfassung: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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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2003.812220