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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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 19; H. 6; S. 838 - 856
Hauptverfasser: Ran Cheng, Yaochu Jin, Narukawa, Kaname, Sendhoff, Bernhard
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2395073