A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling

To approximate the Pareto front, most existing multiobjective evolutionary algorithms store the nondominated solutions found so far in the population or in an external archive during the search. Such algorithms often require a high degree of diversity of the stored solutions and only a limited numbe...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on evolutionary computation Ročník 19; číslo 6; s. 838 - 856
Hlavní autoři: Ran Cheng, Yaochu Jin, Narukawa, Kaname, Sendhoff, Bernhard
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
estimation of distribution algorithms
Gaussian processes
inverse modeling
Inverse problems
Job shops
Multiobjective optimization
Pareto optimization
random grouping
Sociology
Stochastic models
Vectors
inverse modeling
multiobjective optimization (MOO)
random grouping
Gaussian processes (GPs)
Estimation of distribution algorithms (EDAs)
ISSN:1089-778X, 1941-0026
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:To approximate the Pareto front, most existing multiobjective evolutionary algorithms store the nondominated solutions found so far in the population or in an external archive during the search. Such algorithms often require a high degree of diversity of the stored solutions and only a limited number of solutions can be achieved. By contrast, model-based algorithms can alleviate the requirement on solution diversity and in principle, as many solutions as needed can be generated. This paper proposes a new model-based method for representing and searching nondominated solutions. The main idea is to construct Gaussian process-based inverse models that map all found nondominated solutions from the objective space to the decision space. These inverse models are then used to create offspring by sampling the objective space. To facilitate inverse modeling, the multivariate inverse function is decomposed into a group of univariate functions, where the number of inverse models is reduced using a random grouping technique. Extensive empirical simulations demonstrate that the proposed algorithm exhibits robust search performance on a variety of medium to high dimensional multiobjective optimization test problems. Additional nondominated solutions are generated a posteriori using the constructed models to increase the density of solutions in the preferred regions at a low computational cost.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2395073