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...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 19; číslo 6; s. 838 - 856
Hlavní autori:	Ran Cheng, Yaochu Jin, Narukawa, Kaname, Sendhoff, Bernhard
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Sendhoff, Bernhard Yaochu Jin Narukawa, Kaname Ran Cheng
Author_xml	– sequence: 1 surname: Ran Cheng fullname: Ran Cheng organization: Dept. of Comput., Univ. of Surrey, Guildford, UK – sequence: 2 surname: Yaochu Jin fullname: Yaochu Jin email: yaochu.jin@surrey.ac.uk organization: Dept. of Comput., Univ. of Surrey, Guildford, UK – sequence: 3 givenname: Kaname surname: Narukawa fullname: Narukawa, Kaname organization: Honda Res. Inst. Eur., Offenbach, Germany – sequence: 4 givenname: Bernhard surname: Sendhoff fullname: Sendhoff, Bernhard organization: Honda Res. Inst. Eur., Offenbach, Germany
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PublicationYear	2015
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Snippet	To approximate the Pareto front, most existing multiobjective evolutionary algorithms store the nondominated solutions found so far in the population or in an...
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SubjectTerms	Algorithms estimation of distribution algorithms Gaussian processes inverse modeling Inverse problems Job shops Multiobjective optimization Pareto optimization random grouping Sociology Stochastic models Vectors
Title	A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling
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