Metamodel-assisted optimization based on multiple kernel regression for mixed variables

Herrera, Manuel; Guglielmetti, Aurore; Xiao, Manyu; Coelho, Rajan Filomeno

doi:10.1007/s00158-013-1029-z

Cited by 48 publications

(22 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This overview presents important stepping stones [76] and interesting applications in the field, thus showcasing the development. For a more extensive table we refer to the tabular overview in the supplemental material of this article 1 .…”

Section: Strategies For Dealing With Discrete Structuresmentioning

confidence: 99%

Model-based methods for continuous and discrete global optimization

Bartz-Beielstein

Zaefferer

2017

Applied Soft Computing

146

View full text Add to dashboard Cite

show abstract

Section: Strategies For Dealing With Discrete Structuresmentioning

confidence: 99%

Model-based methods for continuous and discrete global optimization

Bartz-Beielstein

Zaefferer

2017

Applied Soft Computing

146

View full text Add to dashboard Cite

show abstract

“…Most data-driven optimization relies on surrogate models to assist the optimizer to guide the search [11]- [13]. Many machine learning methods can be employed to construct surrogates, including artificial neural networks (ANNs) [14], [15] polynomial regression (PR) [16], support vector machines (SVMs) [17], radial basis function (RBF) networks [18]- [20], and Gaussian Processes (GPs). GPs are also known as Kriging or design and analysis of computer experiment models [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Off-line Data-driven Multi-objective Optimization: Knowledge Transfer between Surrogates and Generation of Final Solutions

Yang

Ding

Jin

et al. 2020

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

In off-line data-driven optimization, only historical data is available for optimization, making it impossible to validate the obtained solutions during the optimization. To address these difficulties, this paper proposes an evolutionary algorithm assisted by two surrogates, one coarse model and one fine model. The coarse surrogate aims to guide the algorithm to quickly find a promising sub-region in the search space, whereas the fine one focuses on leveraging good solutions according to the knowledge transferred from the coarse surrogate. Since the obtained Pareto optimal solutions have not been validated using the real fitness function, a technique for generating the final optimal solutions is suggested. All achieved solutions during the whole optimization process are grouped into a number of clusters according to a set of reference vectors. Then, the solutions in each cluster are averaged and outputted as the final solution of that cluster. The proposed algorithm is compared with its three variants and two state-of-the-art off-line datadriven multi-objective algorithms on eight benchmark problems to demonstrate its effectiveness. Finally, the proposed algorithm is successfully applied to an operational indices optimization problem in beneficiation processes.Index Terms-Off-line data-driven optimization; multisurrogate; knowledge transfer; multi-objective evolutionary algorithms.

show abstract

“…In surrogateassisted evolutionary algorithms, surrogate models are employed to replace in part the time-consuming exact function evaluations for saving computational cost because the computational effort required to build and use surrogates is usually much lower than that for expensive fitness evaluations [17], [18]. The most commonly used surrogate models include polynomial regression (PR) [19], also known as response surface methodology [19], support vector machines (SVMs) [20], [21], [22], artificial neural networks (ANNs) [12], [23], [24], radial basis function (RBF) networks [25], [26], [27], [28], [29], and Gaussian Processes (GPs), also referred as to Kriging or design and analysis of computer experiment models [26], [30], [31], [32], [33], [34]. The surrogate-assisted metaheuristic algorithms reported in the literature can be largely classified into the following categories:…”

mentioning

confidence: 99%

“…From the third iteration onward, the following procedure will be undertaken to evaluate the fitness of all particles (lines [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The fitness of all particles in the current swarm will first be approximated by the RBF network and saved as f RBF (x i ), i = 1, 2, .…”

mentioning

confidence: 99%

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Sun

Jin

Cheng

et al. 2017

IEEE Trans. Evol. Computat.

327

131

View full text Add to dashboard Cite

Abstract-Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

show abstract

Metamodel-assisted optimization based on multiple kernel regression for mixed variables

Cited by 48 publications

References 18 publications

Model-based methods for continuous and discrete global optimization

Model-based methods for continuous and discrete global optimization

Off-line Data-driven Multi-objective Optimization: Knowledge Transfer between Surrogates and Generation of Final Solutions

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Contact Info

Product

Resources

About