A surrogate-assisted particle swarm optimization using ensemble learning for expensive problems with small sample datasets

Fan, Chaodong; Hou, Bo; Zheng, Jinhua; Xiao, Leyi; Yi, Lingzhi

doi:10.1016/j.asoc.2020.106242

Cited by 30 publications

(13 citation statements)

References 30 publications

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“…Particles in the swarm are identified by their previous as well as current positions and velocities [ 26 ]. The main stages of the algorithm operational principles include particle fitness evaluation, updating the global as well as individual best and updating the velocity and position of every particle [ 27 ]. Equations (10) and (11) are implemented while updating the velocity and position of swarm particles [ 10 , 28 ].…”

Section: Mathematical Details Of the Hybridized Algorithmsmentioning

confidence: 99%

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

et al. 2021

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Structural transformation and magnetic ordering interplays for emergence as well as suppression of superconductivity in 122-iron-based superconducting materials. Electron and hole doping play a vital role in structural transition and magnetism suppression and ultimately enhance the room pressure superconducting critical temperature of the compound. This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. The developed PS-SVR-RAD model, which utilizes ionic radii (RAD) and the concentrations of dopants as descriptors, shows better performance over the developed PS-SVR-LAT model that employs lattice parameters emanated from structural transformation as descriptors. Using the root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error as performance measuring criteria, the developed PS-SVR-RAD model performs better than the PS-SVR-LAT model with performance improvement of 15.28, 7.62 and 72.12%, on the basis of RMSE, CC and Mean Absolute Error (MAE), respectively. Among the merits of the developed PS-SVR-RAD model over the PS-SVR-LAT model is the possibility of electrons and holes doping from four different dopants, better performance and ease of model development at relatively low cost since the descriptors are easily fetched ionic radii. The developed intelligent models in this work would definitely facilitate quick and precise determination of critical transition temperature of 122-iron-based superconductor for desired applications at low cost with experimental stress circumvention.

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Section: Mathematical Details Of the Hybridized Algorithmsmentioning

confidence: 99%

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

et al. 2021

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“…CALSAPSO, WTA1, and MAES-ExI were developed mainly to address lowdimensional problems (Wang et al 2017). Therefore, the performance of ASAPSO in dealing with high-dimensional problems with dimensions over 30 (Fan et al 2020) was assessed via comparison with different algorithms. Of the existing SAEAs, GPEME ) and iDEaSm (Awad et al 2018) perform well on high-dimensional problems.…”

Section: Scalability Testmentioning

confidence: 99%

An Adaptive Surrogate-Assisted Particle Swarm Optimization for Expensive Problems

2021

Preprint

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To solve engineering problems with evolutionary algorithms, many expensive objective function evaluations (FEs) are required. To alleviate this difficulty, the surrogate-assisted evolutionary algorithm (SAEA) has attracted increasingly more attention in both academia and industry. The existing SAEAs depend on the quantity and quality of the original samples, and it is difficult for them to yield satisfactory solutions within the limited number of FEs. Moreover, these methods easily fall into local optima as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm. In the proposed algorithm, an adaptive surrogate selection method that depends on the comparison between the best existing solution and the latest obtained solution is suggested to ensure the effectiveness of the optimization operations and improve the computational efficiency. Additionally, a model output criterion based on the standard deviation is suggested to improve the robustness and stability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within the limited number of FEs. The performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.

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“…The principle of the Kriging model is similar to that of the RBF model, and it has high prediction accuracy when there are enough samples. It is suitable for dealing with low/medium-dimensional problems and low-order nonlinear problems in high-dimensional space (Fan et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The key issue to be addressed in SAEAs is determining which individuals should be selected for evaluation or reevaluation using the real fitness function. The most direct method is to evaluate those individuals with a good fitness value or high approximation accuracy according to the results obtained from the surrogate model (Fan et al 2020;Zhang et al 2015). In addition, some representative individuals have been selected in different research.…”

Section: Introductionmentioning

confidence: 99%

An adaptive surrogate-assisted particle swarm optimization for expensive problems

2021

Soft Comput

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To solve engineering problems with evolutionary algorithms, many expensive objective function evaluations (FEs) are required. To alleviate this difficulty, the surrogateassisted evolutionary algorithm (SAEA) has attracted increasingly more attention in both academia and industry. The existing SAEAs depend on the quantity and quality of the original samples, and it is difficult for them to yield satisfactory solutions within the limited number of FEs. Moreover, these methods easily fall into local optima as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm. In the proposed algorithm, an adaptive surrogate selection method that depends on the comparison between the best existing solution and the latest obtained solution is suggested to ensure the effectiveness of the optimization operations and improve the computational efficiency. Additionally, a model output criterion based on the standard deviation is suggested to improve the robustness and stability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within the limited number of FEs. The performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.

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A surrogate-assisted particle swarm optimization using ensemble learning for expensive problems with small sample datasets

Cited by 30 publications

References 30 publications

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

An Adaptive Surrogate-Assisted Particle Swarm Optimization for Expensive Problems

An adaptive surrogate-assisted particle swarm optimization for expensive problems

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