Local SVM Constraint Surrogate Models for Self-adaptive Evolution Strategies

Poloczek, Jendrik; Krämer, Oliver

doi:10.1007/978-3-642-40942-4_15

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…In the field of model-assisted optimization algorithms for constrained problems, support vector machines (SVMs) have been used by Poloczek and Kramer [26]. They make use of SVMs as a classifier for predicting the feasibility of solutions, but achieve only slight improvements.…”

Section: Related Workmentioning

confidence: 99%

Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

Bagheri,

Konen,

Emmerich

et al. 2015

Preprint

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Constrained optimization of high-dimensional numerical problems plays an important role in many scientific and industrial applications. Function evaluations in many industrial applications are severely limited and no analytical information about objective function and constraint functions is available. For such expensive black-box optimization tasks, the constraint optimization algorithm COBRA was proposed, making use of RBF surrogate modeling for both the objective and the constraint functions. COBRA has shown remarkable success in solving reliably complex benchmark problems in less than 500 function evaluations. Unfortunately, COBRA requires careful adjustment of parameters in order to do so.In this work we present a new self-adjusting algorithm SACOBRA, which is based on COBRA and capable to achieve high-quality results with very few function evaluations and no parameter tuning. It is shown with the help of performance profiles on a set of benchmark problems (G-problems, MOPTA08) that SACOBRA consistently outperforms any COBRA algorithm with fixed parameter setting. We analyze the importance of the several new elements in SACOBRA and find that each element of SACOBRA plays a role to boost up the overall optimization performance. We discuss the reasons behind and get in this way a better understanding of high-quality RBF surrogate modeling.

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Section: Related Workmentioning

confidence: 99%

Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

Bagheri,

Konen,

Emmerich

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, the constraint boundary is shifted to the infeasible region by a small distance to limit this error. Another study [30] proposed a probability which is called the influence coefficient to prevent the misclassification of the surrogate model. In 2018, Wang et al [31] introduced a technique in which the boundary is adjusted to decrease the possibility of classifying feasible solutions to be infeasible.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier

Nguyen¹,

Vu²

2023

Computer Modeling in Engineering &Amp; Sciences

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Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses that must be conducted. Building a surrogate model to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem. However, most existing surrogate models have been designed based on regression techniques. This paper proposes a novel method, called CaDE, which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution (DE) optimization. The proposed method is separated into two stages. During the first optimization stage, the original DE is implemented as usual, but all individuals produced in this phase are stored as inputs of the training data. Based on design constraints verification, these individuals are labeled as "safe" or "unsafe" and their labels are saved as outputs of the training data. When collecting enough data, an AdaBoost model is trained to evaluate the safety state of structures. This model is then used in the second stage to preliminarily assess new individuals, and unpromising ones are rejected without checking design constraints. This method reduces unnecessary structural analyses, thereby shortens the optimization process. Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness. The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants. The reduction rate of five examples ranges from 18 to over 50%. Moreover, the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.

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“…Jin et al pointed out that surrogates can be applied to almost all operations of EAs [8], consisting of population initialization, crossover, mutation, local search, fitness evaluation and so on [9−11]. A variety of machine learning models have been used to construct surrogates, such as polynomial regression (PR) [12,13], Gaussian processes (GP) [14−16] (also known as Kriging [17,18]), radial basis function (RBF) [19], artificial neural networks (ANNs) [20−22], and support vector machine (SVM) [23,24]. Moreover, ensemble of surrogates is also used to enhance the performance of SAEAs [25,26].…”

Section: Introductionmentioning

confidence: 99%

Data-driven evolutionary sampling optimization forexpensive problems

Zhen

Gong

2021

J. of Syst. Eng. Electron.

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Surrogate models have shown to be effective in assisting evolutionary algorithms (EAs) for solving computationally expensive complex optimization problems. However, the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved. A data-driven evolutionary sampling optimization (DESO) framework is proposed, where at each generation it randomly employs one of two evolutionary sampling strategies, surrogate screening and surrogate local search based on historical data, to effectively balance global and local search. In DESO, the radial basis function (RBF) is used as the surrogate model in the sampling strategy, and different degrees of the evolutionary process are used to sample candidate points. The sampled points by sampling strategies are evaluated, and then added into the database for the updating surrogate model and population in the next sampling. To get the insight of DESO, extensive experiments and analysis of DESO have been performed. The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions. Besides, DESO is applied to an airfoil design problem to show its effectiveness.

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Local SVM Constraint Surrogate Models for Self-adaptive Evolution Strategies

Cited by 11 publications

References 10 publications

Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier

Data-driven evolutionary sampling optimization forexpensive problems

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