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.
Keywords
Benchmark (surveying)Multi-objective optimizationRank (graph theory)Mathematical optimizationRanking (information retrieval)Pareto principleGenetic algorithmMathematicsEvolutionary algorithmAlgorithmComputer scienceMachine learningCombinatorics
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Publication Info
- Year
- 2003
- Type
- article
- Volume
- 7
- Issue
- 4
- Pages
- 325-343
- Citations
- 178
- Access
- Closed
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Cite This
Haiming Lu,
Gary G. Yen
(2003).
Rank-density-based multiobjective genetic algorithm and benchmark test function study.
IEEE Transactions on Evolutionary Computation
, 7
(4)
, 325-343.
https://doi.org/10.1109/tevc.2003.812220
Identifiers
- DOI
- 10.1109/tevc.2003.812220