A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation

In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called weighted optimization framework is intended to serve as a generic method that can be used with any population-based metaheuristic algorithm. A...

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Vydáno v:IEEE Transactions on Evolutionary Computation Ročník 22; číslo 2; s. 260 - 275
Hlavní autoři: Zille, Heiner, Ishibuchi, Hisao, Mostaghim, Sanaz, Nojima, Yusuke
Médium: Journal Article
Jazyk:angličtina
japonština
Vydáno: IEEE 01.04.2018
Institute of Electrical and Electronics Engineers (IEEE)
Témata:
Algorithm design and analysis
Benchmark testing
Computer science
Diversity reception
Electronic mail
Large-scale optimization (LSO)
many-variable optimization
metaheuristic framework
multiobjective optimization
Optimization
Search problems
variable grouping
weighting
ISSN:1089-778X, 1941-0026
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Shrnutí:In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called weighted optimization framework is intended to serve as a generic method that can be used with any population-based metaheuristic algorithm. After explaining some general issues of large-scale optimization, we introduce a problem transformation scheme that is used to reduce the dimensionality of the search space and search for improved solutions in the reduced subspace. This involves so-called weights that are applied to alter the decision variables and are also subject to optimization. Our method relies on grouping mechanisms and employs a population-based algorithm as an optimizer for both original variables and weight variables. Different grouping mechanisms and transformation functions within the framework are explained and their advantages and disadvantages are examined. Our experiments use test problems with 2-3 objectives 40-5000 variables. Using our approach on three well-known algorithms and comparing its performance with other large-scale optimizers, we show that our method can significantly outperform most existing methods in terms of solution quality as well as convergence rate on almost all tested problems for many-variable instances.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2704782