Multi/many-objective evolutionary algorithm assisted by radial basis function models for expensive optimization

Li, Jinglu; Wang, Peng; Dong, Huachao; Shen, Jiangtao

doi:10.1016/j.asoc.2022.108798

Cited by 15 publications

(4 citation statements)

References 46 publications

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“…Te authors concluded that the model produced not only predicted target values but also valuable uncertainty information. Tis additional information facilitates a more informed explorationexploitation balance by employing a lower confdence constraint flling criterion in the algorithm [18]. Te genetic algorithm was developed by John Holland in 1960 and was developed for the frst time in 1975 when many improvements were made by Pazooki et al [19].…”

Section: Introductionmentioning

confidence: 99%

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Kriging and Radial Basis Function Models for Optimized Design of UAV Wing Fences to Reduce Rolling Moment

Moghimi Esfand-Abadi,

Djavareshkian,

Madani

2024

International Journal of Intelligent Systems

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In the present study, the effects of the wing fence on the wing tip vortices and control surfaces located at the tip of the wing in a flying wing aircraft have been investigated using a numerical method. For the size of the fences, the average dimensions extracted from the wing tip vortices at different angles of attack are used. The basic determining parameter is the rolling torque coefficient, which is tried to be shown by employing a parametric study of the flow behavior in different situations of fence placement. These effects on the rolling torque of the aircraft are measured due to the presence of the split drag rudder control system. In this study, the fences were installed at three different heights and three different positions along the length of the wing, which were investigated at angles of attack of 7 to 16 degrees. The next stage of the research is to design the dimensions of the fence using the single-objective optimization method (a method to find the best solution for a problem with a specific goal). The designing of the fences at three points based on the dimensions of the wing tip vortex is carried out with the computational fluid dynamics (CFD) method (CFD is a computational method that uses physical laws to predict the behavior of fluids.). The aim of this research is to achieve the best design that converges to an optimal solution with minimum time and cost (CFD solution is long). However, CFD analysis requires a lot of computational time. To address this challenge, we employed a hybrid learning model comprising the radial basis function (RBF), a type of artificial neural network, and Kriging, a Gaussian process-based interpolation technique. The dataset for training the hybrid model was obtained from numerical solutions of CFD simulations involving a fence placed at various locations on the wing. Additionally, a genetic algorithm was employed as the optimization method in all instances where it was required. Using the power of machine learning techniques helped us identify the optimal placement of the fence to prevent it from being engulfed by the vortex and to optimize the utilization of the split drag system, yielding significant improvements.

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Section: Introductionmentioning

confidence: 99%

“…Namura et al used the Kriging method to optimize the vortex generators on the wing of the aircraft to maximize the Bera coefcient [10]. Building upon the optimization methods presented in (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) and prior works, this article proposes a novel approach to address the aforementioned issue.…”

Section: Introductionmentioning

confidence: 99%

Kriging and Radial Basis Function Models for Optimized Design of UAV Wing Fences to Reduce Rolling Moment

Moghimi Esfand-Abadi,

Djavareshkian,

Madani

2024

International Journal of Intelligent Systems

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“…The other type is where the output of the surrogate model is the type of the solution, such as CPS-MOEA (Zhang, Zhou et al 2015), all solutions are classified as "good" or "poor" based on non-dominated sorting, and a surrogate model is then built to screen offspring. Commonly used models for assisting the process of many-objective optimization include Radial Basis Functions (RBF) (Li, Wang et al 2022), Kriging models (Liu and Wang 2022), Support Vector Machines (SVM) (Yang, Huang et al 2022), Polynomial…”

Section: Introductionmentioning

confidence: 99%

A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction

Dali,

Yifei,

et al. 2023

Preprint

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In expensive Many-objective Optimization Problems, Surrogate-Assisted Evolutionary Algorithms (SAEAs) are often used to reduce the number of original Function Evaluations (FE). However, traditional SAEAs may suffer from long running times due to the complex computational demands of the surrogate models themselves. In this paper, we propose a novel algorithm called " A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction (DSAG)", aimed at further reducing the running time of SAEAs. The proposed algorithm employs two surrogate models: one for predicting the diversity of solutions and another for predicting the convergence value of solutions, and then sorts the solutions based on the prediction results. Afterwards, the algorithm adaptively adjusts the type and number of candidate solutions according to the "Convergence-Related Average Move Distance (CAMD)" proposed in this paper. This algorithm can adaptively bias towards either the convergence exploration stage or the diversity exploration stage. Compared with other classical surrogate-assisted evolutionary algorithms, the proposed algorithm first categorizes the decision variables and uses the categorized data to train two surrogate models, which makes the overall complexity and running time of the algorithm superior to others. Finally, we compared the performance of this algorithm on different problems using benchmark test functions to verify its ability to explore diversity and improve convergence. Then, we compared the running time of the algorithm with changes in the target dimension, decision variable dimension, and number of Original FE on the same test problems, and demonstrated the superior performance of the algorithm.

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“…Surrogate models are also known as meta-models. Many machine learning models are used in SAEAs, such as polynomial response surface (PRS) [50], radial basis function (RBF) [25,28], artificial neural networks [4], support vector machines [36], and Gaussian/Kriging model [14,49]. Moreover, many efforts have been studied to embed the multi-surrogate model or ensemble model into SAEAs frameworks to improve the accuracy of fitness approximation [54].…”

Section: Introductionmentioning

confidence: 99%

An ensemble surrogate-assisted adaptive reference point guided evolutionary algorithm for expensive many-objective irregular problem

You

Niu

Tang

et al. 2023

Preprint

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Surrogate-assisted evolutionary algorithms (SAEAs) are one effective method for solving expensive optimization problems. However, there has been little attention to expensive many-objective irregular problems. To address this issue, we propose an ensemble surrogate-assisted adaptive reference point guided evolutionary algorithm for dealing with expensive many-objective irregular problems. Firstly, a reference point adaptation method is adopted in the proposed algorithm to adjust the reference point for calculating indicators and guide the search process. Secondly, the enhanced inverted generational distance (IGD-NS) indicator is improved by using the modified distance to obey the Pareto compliant, which can maintain a balance between convergence and diversity in the population. Thirdly, an infill sampling criterion is designed to select elite individuals for re-evaluation in case the Pareto fronts are irregular. The added elite individuals update the ensemble surrogate model, which is expected to assist the algorithm in efficiently finding the Pareto optimal solutions in a limited computational resource. Finally, experimental results on several benchmark problems demonstrate that the proposed algorithm performs well in solving expensive many-objective optimization problems with irregular and regular Pareto fronts. A real-world application problem also confirms the effectiveness and competitiveness of the proposed algorithm.

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Multi/many-objective evolutionary algorithm assisted by radial basis function models for expensive optimization

Cited by 15 publications

References 46 publications

Kriging and Radial Basis Function Models for Optimized Design of UAV Wing Fences to Reduce Rolling Moment

Kriging and Radial Basis Function Models for Optimized Design of UAV Wing Fences to Reduce Rolling Moment

A Many-Objective Optimization Evolutionary Algorithm Based on Double Surrogate-Assisted Adaptive Guiding Evolutionary Direction

An ensemble surrogate-assisted adaptive reference point guided evolutionary algorithm for expensive many-objective irregular problem

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