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Surrogate-assisted evolutionary sampling particle swarm optimization for high-dimensional expensive optimization

NEURAL COMPUTING & APPLICATIONS 2023年 1-17页

作者： Huang, Kuihua Zhen, Huixiang Gong, Wenyin Wang, Rui Bian, Weiwei Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Beijing Mech Equipment Res Inst Equipment Gen Technol Lab Beijing 100854 Peoples R China

Surrogate-assisted evolutionary algorithms have been widely employed for solving expensive optimization problems. To address high-dimensional expensive optimization problems, we propose an evolutionary sampling-assisted particle swarm optimization method, termed ESPSO. ESPSO consists of some evolutionary sampling-assisted strategies. It first improves the initialized population with some elite samples by evolutionary sampling. Secondly, during the optimization process, the method builds a local radial basis function model using the personal historical optimal data of the population to approximate the objective function landscape. Finally, surrogate-assisted local search and surrogate-assisted trust region search are designed to find promising candidate solutions for replacing individuals in the population to accelerate the search process. Behavioral research experiments of ESPSO verified these strategies have led to improvements in the search efficiency of the algorithm in various aspects, such as initialization, population update, and optimal solution promotion. We compared ESPSO with five state-of-the-art SAEAs using 18 benchmark functions, which show that ESPSO outperforms the other compared SAEAs and get the best average ranking of 2.194.

关键词： evolutionary sampling Expensive optimization Particle swarm optimization Radial basis function Surrogate model

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Neighborhood evolutionary sampling with dynamic repulsion for expensive multimodal optimization

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INFORMATION SCIENCES 2023年第1期630卷 82-97页

作者： Zhen, Huixiang Xiong, Shijie Gong, Wenyin Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Many real-world applications can be categorized as expensive multimodal optimization problems which not only have multiple global optima, but also their objective functions are time-consuming. To address them, the article proposed a neighborhood evolutionary sampling framework with dynamic repulsion, termed DR-NESO, which consists of two main parts. On one hand, DR-NESO uses neighborhood evolutionary sampling strategies to generate candidate solutions for real fitness evaluation, balance exploration and exploitation, and accelerate population rapidly converge to multiple global optima. On the other hand, when potential global optimal solutions are identified and archived during optimization, DR-NESO dynamically sets taboo areas based on them. Individuals located in taboo areas are moved to other promising areas by a surrogate-assisted elite restart strategy. Based on this framework, expensive multimodal optimization problems can be solved well in a limited real fitness evaluation budget. We compared the approach with state-of-the-art expensive multimodal and multimodal algorithms on the CEC2013 benchmark. The result shows DR-NESO has the best performance.

关键词： evolutionary sampling Expensive multimodal optimization Neighborhood Surrogate Dynamic repulsion

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Data-driven evolutionary sampling optimization for expensive problems

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Journal of Systems Engineering and Electronics 2021年第2期32卷 318-330页

作者： ZHEN Huixiang GONG Wenyin WANG Ling School of Computer Science China University of GeosciencesWuhan 430074China Department of Automation Tsinghua UniversityBeijing 100084China

Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization ***,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 *** 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 *** sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next *** get the insight of DESO,extensive experiments and analysis of DESO have been *** proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 ***,DESO is applied to an airfoil design problem to show its effectiveness.

关键词： evolutionary algorithm(EA) surrogate model datadriven evolutionary sampling airfoil design

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A Novel evolutionary sampling Assisted Optimization Method for High-Dimensional Expensive Problems

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2019年第5期23卷 815-827页

作者： Wang, Xinjing Wang, G. Gary Song, Baowei Wang, Peng Wang, Yang Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Simon Fraser Univ Sch Mechatron Syst Engn Surrey BC V3T 0A3 Canada Northwestern Polytech Univ Sch Management Xian 710072 Shaanxi Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are promising methods for solving high-dimensional expensive problems. The basic idea of SAEAs is the integration of nature-inspired searching ability of evolutionary algorithms and prediction ability of surrogate models. This paper proposes a novel evolutionary sampling assisted optimization (ESAO) method which combines the two abilities to consider global exploration and local exploitation. Differential evolution is employed to generate offspring using mutation and crossover operators. A global radial basis functions surrogate model is built for prescreening of the offspring's objective function values and identifying the best one, which will be evaluated with the true function. The best offspring will replace its parent's position in the population if its function value is smaller than that of its parent. A local surrogate model is then built with selected current best solutions. An optimizer is applied to find the optimum of the local model. The optimal solution is then evaluated with the true function. Besides, a better point found in the local search will be added into the population in the global search. Global and local searches will alternate if one search cannot lead to a better solution. Comprehensive analysis is conducted to study the mechanism of ESAO and insights are gained on different local surrogates. The proposed algorithm is compared with two state-of-the-art SAEAs on a series of high-dimensional problems and results show that ESAO behaves better both in effectiveness and robustness on most of the test problems. Besides, ESAO is applied to an airfoil optimization problem to show its effectiveness.

关键词： Computational modeling Optimization Sociology Statistics Predictive models Adaptation models evolutionary algorithms (EAs) evolutionary sampling high-dimensional expensive problems surrogate models

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evolutionary sampling: A novel way of machine learning within a probabilistic framework

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INFORMATION SCIENCES 2015年 299卷 262-282页

作者： Xie, Zhenping Sun, Jun Palade, Vasile Wang, Shitong Liu, Yuan Jiangnan Univ Sch Digital Media Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China Univ Oxford Dept Comp Sci Oxford OX1 3QD England

In many traditional machine learning methods, sampling is only a process of acquiring training data. However, some studies (on sequential Markov chains and particle filters) have demonstrated that sampling can be used for solving some intractable optimization problems in classical learning methods. Along this line of thinking, the relationships between sampling and learning are theoretically exploited in this paper, wherein the key feature of the sampling process is selecting representative samples from original data that can be modeled by a probability distribution. In theory, acquiring reliable samples is not an easy task for an arbitrary probability distribution. Motivated by approaches in evolutionary computation, rejection sampling and function approximation, a novel sampling strategy, called the evolutionary sampling, is proposed in this paper, and a machine learning method, called the evolutionary sampling approach (ESA), is put forward afterwards. Within ESA, a computing model, called the support sample model (SSM), is presented as well and is used to approximate an original density function. Accordingly, a concrete implementation of an evolutionary sampling approach (ESA) is proposed to seek the optimal model parameters of the SSM. Benefiting from the combination of rejection sampling and evolutionary searching, the ESA can theoretically converge to the optimal solution by minimizing the total variation distance, and can do this with high computational efficiency.. Moreover, the normalized factor of a density function can be automatically estimated with high precision within the ESA. As a result, the ESA may be suitable for machine learning problems that could be transformed into density function approximation problems within a probabilistic framework. In addition, derived from the rejection sampling strategy, the ESA can also have online learning abilities required by large-scale data stream processing tasks. Theoretical analyses and application studies ar

关键词： evolutionary sampling Support sample model Monte Carlo Markov chain Rejection sampling Online learning Particle swarm optimization

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A Learnable Prior Improves Inverse Tumor Growth Modeling

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2025年第3期44卷 1297-1307页

作者： Weidner, Jonas Ezhov, Ivan Balcerak, Michal Metz, Marie-Christin Litvinov, Sergey Kaltenbach, Sebastian Feiner, Leonhard Lux, Laurin Kofler, Florian Lipkova, Jana Latz, Jonas Rueckert, Daniel Menze, Bjoern Wiestler, Benedikt Tech Univ Munich Dept Comp Sci D-81675 Munich Germany Tech Univ Munich Ctr Translat Canc Res TranslaTUM D-81675 Munich Germany Univ Zurich Dept Quant Biomed CH-8057 Zurich Switzerland Tech Univ Munich Sch Med & Hlth Dept Neuroradiol Munich Germany Harvard Univ Sch Engn & Appl Sci Cambridge MA USA Helmholtz Zentrum Munchen Helmholtz AI D-85764 Oberschleissheim Germany Univ Calif Irvine Dept Pathol Irvine CA 92617 USA Univ Manchester Dept Math Manchester M13 9PL England Imperial Coll London Dept Comp Biomed Image Anal Grp London SW7 2AZ England Tech Univ Munich Klinikum rechts Isar D-81675 Munich Germany

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%.

关键词： Tumors Brain modeling Computational modeling Predictive models Mathematical models Training Radiation therapy Robustness Image segmentation Biological system modeling Individualized brain tumor modeling learnable prior evolutionary sampling CMA-ES MRI inverse biophysics

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Surrogate-Assisted Differential Evolution with multiple sampling mechanisms for high-dimensional expensive problems

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INFORMATION SCIENCES 2025年 687卷

作者： Yu, Laiqi Meng, Zhenyu Fujian Univ Technol Inst Artificial Intelligence Fuzhou Peoples R China

Recently, Surrogate-Assisted evolutionary Algorithms (SAEAs) have been widely employed in solving Expensive Optimization Problems (EOPs) due to their efficiency in obtaining satisfactory solutions with limited resources. By leveraging historical data to construct surrogate models for approximation, SAEAs can significantly reduce the number of expensive fitness evaluations for EOPs. A hierarchical SAEA optimization framework based on evolutionary sampling methods has achieved remarkable success in High-dimensional EOPs (HEOPs), which can effectively balance the exploration and exploitation capabilities. However, the majority of existing hierarchical SAEAs focus on either switching between different sampling strategies or enhancing a single sampling strategy, potentially overlooking the potential to improve multiple sampling strategies simultaneously. In this paper, we propose a Surrogate-Assisted Differential Evolution with Multiple sampling Mechanisms (SADE-MSM) to tackle HEOPs, incorporating three sampling strategies with different mechanisms. The contributions of SADE-MSM are summarized as follows: 1) A centroid sampling method is applied before iterative optimization to enhance the early exploration ability;2) An improved global prescreening sampling strategy is introduced to balance the exploration and exploitation capabilities;3) A local search sampling with the adaptive optimal region strategy is proposed, significantly improving the exploitation ability. To validate the performance of SADEMSM, we compared it with the state-of-the-art SAEAs on benchmark problems with dimensions ranging from 30 to 500. Experimental results demonstrate that SADE-MSM has a significant performance superiority.

关键词： evolutionary sampling High-dimensional expensive optimization Surrogate-assisted evolutionary algorithms

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Using evolutionary sampling to mine imbalanced data

Using evolutionary sampling to mine imbalanced data

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6th International Conference on Machine Learning and Applications

作者： Drown, Dennis J. Khoshgoftaar, Taghi M. Narayanan, Rarnaswarny Florida Atlantic Univ Boca Raton FL 33431 USA

ISBN: (纸本)9780769530697

Class imbalance tends to cause inferior performance in data mining learners. evolutionary sampling is a technique which seeks to counter this problem by using genetic algorithms to evolve a reduced sample of a complete dataset to train a classification model. evolutionary sampling works to remove noisy and duplicate instances so that the sampled training data will produce a superior classifier We propose this novel technique as a method to handle severe class imbalance in data mining. This paper presents our research into the the use of evolutionary sampling with C4.5 decision trees and compares the technique's performance with random undersampling.

关键词： evolutionary sampling class imbalance sampling genetic algorithms decision trees

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