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Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms.
- Source :
- Multiobjective Problem Solving from Nature; 2008, p331-355, 25p
- Publication Year :
- 2008
-
Abstract
- Model-based multiobjective optimization is one class of metaheuristics for solving multiobjective optimization problems, where a probabilistic model is built from the current distribution of the solutions and new candidate solutions are generated from the model. One main difficulty in model-based optimization is constructing a probabilistic model that is able to effectively capture the structure of the problems to enable efficient search. This chapter advocates a new type of probabilistic model that takes the regularity in the distribution of Pareto-optimal solutions into account. We compare our model to two other model-based multiobjective algorithms on a number of test problems to demonstrate that it is scalable to high-dimensional optimization problems with or without linkage linkage among the design variables. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540729631
- Database :
- Supplemental Index
- Journal :
- Multiobjective Problem Solving from Nature
- Publication Type :
- Book
- Accession number :
- 33678446
- Full Text :
- https://doi.org/10.1007/978-3-540-72964-8_16