ASM-MOMA: Multiobjective memetic algorithm with aggregate surrogate model

Pilát, Martin; Neruda, Roman

doi:10.1109/cec.2011.5949753

Cited by 19 publications

(18 citation statements)

References 14 publications

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“…In addition, a surrogate can be used to estimate the rank of individuals [31], or some other quality measure, e.g. distance to the nondominated solutions [38], [39], [40], or hypervolume [2]. The third challenge is the computational cost for constructing the surrogate, which is often neglected in SAEAs.…”

Section: Introductionmentioning

confidence: 99%

“…During the years 2008-2015, only three algorithms [6], [38], [41] have been tested on multi-objective benchmark problems with more than three objectives. While many industrial problems, e.g., optimization of the controller of a hybrid car [36], involve more than three computationally expensive objectives, surrogate-assisted evolutionary optimization of many-objective problems has not attracted much attention in the evolutionary computation community and SAEAs developed so far cannot be directly extended to many-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

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A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Chugh

Jin

Miettinen

et al. 2018

IEEE Trans. Evol. Computat.

468

185

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Abstract-We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art surrogate-assisted evolutionary algorithms on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Chugh

Jin

Miettinen

et al. 2018

IEEE Trans. Evol. Computat.

468

185

View full text Add to dashboard Cite

show abstract

“…ASM-MOMA [15] is a multiobjective memetic algorithm with aggregate surrogate model. The memetic operator uses a meta-model of the search space trained on a set, which is based on previously evaluated individuals and their distance to the currently known Pareto front.…”

Section: A Asm-momamentioning

confidence: 99%

“…Here, we describe our multiobjective memetic algorithms: ASM-MOMA [15] with single global meta-model and LAMM-MMA [16] with local aggregate meta-models.…”

Section: Algorithms With Aggregate Meta-modelsmentioning

confidence: 99%

Improving many-objective optimizers with aggregate meta-models

Pilát

Neruda

2011

2011 11th International Conference on Hybrid Intelligent Systems (HIS)

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In the field of multi-objective optimization there have been attempts to reduce the number of objective function evaluations by the use of surrogate models. However, in manyobjective optimization, this work still has to be done to make the optimizers more practically usable.In this paper we show, that aggregate meta-models can be used even for the many-objective optimization and that they can also improve the performance of the many-objective optimizer. Moreover, meta-models are discussed from another point of view and compared to scalarization techniques in many-objective optimization.Two algorithms using our models are compared to IBEA on a set of selected benchmark functions with 5, 10, and 15 objectives.Index Terms-Many-objective optimization, meta-models, surrogate models, evolutionary algorithms.

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“…In this paper, we propose a new variant of ASM-MOMA [3] with local models used instead of a single global one, as we used in ASM-MOMA. We call this variant LAMM-MMA.…”

Section: Algorithm Descriptionmentioning

confidence: 99%