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surrogate-assisted evolutionary algorithm with dimensionality reduction method for water flooding production optimization

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年第0期185卷 106633-000页

作者： Chen, Guodong Zhang, Kai Xue, Xiaoming Zhang, Liming Yao, Jun Sun, Hai Fan, Ling Yang, Yongfei China Univ Petr East China 66 Changjiang West Rd Qingdao 266580 Shandong Peoples R China

The objective of oil reservoir production optimization is finding optimal scheme of each well to maximize the net present value (NPV) or the hydrocarbon production. Various optimization methods have been proposed to address production optimization problems. surrogate-assisted evolutionary algorithms have gained increasing attention due to the great performance in reducing the computational resource. However, when dealing with high dimensional optimization problems, surrogate model requires a large number of memories and becomes computationally prohibitive and building a high-quality surrogate model become difficult because of the "curse of dimensionality". In this paper, a new framework is proposed based on surrogate-assisted evolutionary algorithm and dimensionality reduction to maximize the expected NPV in life-cycle production optimization. To take advantage of surrogate model in low dimension, Sammon mapping is applied to map high-dimensional decision variables to lower dimensions. Then the search can focus on smaller area guided by surrogate model. The optimization results of two examples show the great performance of the proposed method in comparison with classical evolutionary algorithm and different popular dimensionality reduction methods.

关键词： surrogate model Production optimization Dimensionality reduction surrogate-assisted evolutionary algorithm Differential evolution

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A domain-transformed surrogate-assisted differential evolutionary algorithm for hyperparameter optimisation of satellite handover strategy

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INFORMATION SCIENCES 2025年 700卷

作者： Yang, Zhe Deng, Libao Li, Chunlei Qin, Yifan Zhang, Lili Harbin Inst Technol Sch Informat Sci & Engn Weihai 264209 Shandong Peoples R China Dublin City Univ Sch Comp Dublin D09 V209 Ireland

With the rapid advancement of low-Earth-orbit (LEO) satellite technology, satellite phone calls have become increasingly widespread. However, this progress introduces new challenges: the high velocity of LEO satellites necessitates frequent reconnections between terminals and satellites, which can adversely affect communication quality. Moreover, the frequent signal measurements required to maintain connectivity significantly increase the energy consumption of the terminals. To address these challenges, this paper proposes a terminal measurement and satelliteswitching strategy. The strategy aims to minimize energy consumption during switching while preserving a high level of user experience. The strategy is based on predictions of satellite cell visibility time and beam visibility time. To further optimise the hyperparameters of this method, we established a simulation-based hyperparameter optimisation model and designed a domaintransformed surrogate-assisted differential evolutionary algorithm (DT-SADE). The algorithm utilises a radial basis function (RBF) model as a surrogate for global search and a Kriging model for local search, and uses domain transformation to address discrepancies between the simulation and surrogate models. Experimental results demonstrate that the proposed method outperforms comparative algorithms across multiple performance metrics, and that hyperparameter optimisation further enhances its performance, highlighting the significance and effectiveness of hyperparameter optimisation.

关键词： Domain Transform surrogate-assisted evolutionary algorithm Hyperparameter Optimisation Satellite Handover Strategy

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surrogate-assisted evolutionary algorithm with decomposition-based local learning for high-dimensional multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Shen, Jiangtao Wang, Peng Dong, Huachao Wang, Wenxin Li, Jinglu Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

When the evolutionary algorithm is applied to handle high -dimensional expensive multi -objective optimization problems (MOPs), population evolution is crucial since it controls exploration and exploitation and decides if promising candidate solutions could be generated. However, little attention has been paid to this issue, evolution operators based on Genetic algorithm (GA) and Differential Evolution (DE) are still the two most common approaches, whose convergence on high -dimensional MOPs with a limited number of fitness evaluations remains challenging. In this paper, we propose a decomposition -based local learning strategy to accelerate convergence in the high -dimensional search space of MOPs. Specifically, an individual is updated by learning from one of the best solutions of its corresponding local area based on the multi -objective decomposition approach. Accordingly, a surrogate -assisted evolutionary algorithm is proposed for better solving expensive high -dimensional MOPs. Experimental studies on MOPs with up to 100 decision variables and with 300 fitness evaluations demonstrate the effectiveness of the proposed method. Furthermore, we use the proposed method to solve a 2 -objective shape design problem of the blended wing -body underwater glider (BWBUG) with 39 decision variables, and an impressive solution set is obtained.

关键词： surrogate-assisted evolutionary algorithm Expensive high-dimensional multi-objective optimization Decomposition-based approach Multi-objective shape design

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Efficient Generalized surrogate-assisted evolutionary algorithm for High-Dimensional Expensive Problems

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2020年第2期24卷 365-379页

作者： Cai, Xiwen Gao, Liang Li, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Engineering optimization problems usually involve computationally expensive simulations and many design variables. Solving such problems in an efficient manner is still a major challenge. In this paper, a generalized surrogate-assisted evolutionary algorithm is proposed to solve such high-dimensional expensive problems. The proposed algorithm is based on the optimization framework of the genetic algorithm (GA). This algorithm proposes to use a surrogate-based trust region local search method, a surrogate-guided GA (SGA) updating mechanism with a neighbor region partition strategy and a prescreening strategy based on the expected improvement infilling criterion of a simplified Kriging in the optimization process. The SGA updating mechanism is a special characteristic of the proposed algorithm. This mechanism makes a fusion between surrogates and the evolutionary algorithm. The neighbor region partition strategy effectively retains the diversity of the population. Moreover, multiple surrogates used in the SGA updating mechanism make the proposed algorithm optimize robustly. The proposed algorithm is validated by testing several high-dimensional numerical benchmark problems with dimensions varying from 30 to 100, and an overall comparison is made between the proposed algorithm and other optimization algorithms. The results show that the proposed algorithm is very efficient and promising for optimizing high-dimensional expensive problems.

关键词： Optimization Genetic algorithms evolutionary computation Computational modeling Prediction algorithms Partitioning algorithms Search methods High-dimensional expensive problems multiple surrogates prescreening strategy simplified Kriging surrogate-assisted evolutionary algorithm surrogate-guided crossover operation trust region method

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A surrogate-assisted evolutionary algorithm with an adaptive sample selection strategy for wind farm layout optimization

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2023年第9期20卷 966-977页

作者： Li, Xuemei Liu, Mingyang Li, Shaojun East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

When using evolutionary algorithms to optimize wind farm layouts, it is computationally expensive to use computational fluid dynamics (CFD) model to evaluate different wind farm layouts. To alleviate the difficulty, surrogate models are used to replace CFD model. Such algorithms are called surrogate-assisted evolutionary algorithms (SAEAs). In existing SAEAs, constructing a global surrogate for high-dimensional problems is not reliable due to the curse of dimensionality. Additionally, a local surrogate model constructed with a fixed number of samples has limitations. To address these problems, an SAEA combining global exploration and local exploitation is proposed. The global exploration is based on an adaptive surrogate-assisted particle swarm optimization algorithm, while the local exploitation is based on a local RBF-assisted simulated annealing algorithm. In building a local RBF model, according to the current function evaluation, a certain amount of outstanding samples are selected from the data set. 10 benchmark functions with different modalities and dimensions have been tested to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm performs well for most benchmark functions. The proposed algorithm was applied to optimize wind farm layout, and achieved an 8.3% increase in annual net power generation.

关键词： Wind farm layout surrogate-assisted evolutionary algorithm radial basis function simulated annealing algorithm particle swarm optimization

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A bagging-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization

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NEURAL COMPUTING & APPLICATIONS 2022年第14期34卷 12097-12118页

作者： Liu, Yuanchao Liu, Jianchang Tan, Shubin Yang, Yongkuan Li, Fei Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Xiamen Univ Technol Sch Elect Engn & Automat Xiamen Peoples R China Anhui Univ Technol Dept Elect & Informat Engn Maanshan Anhui Peoples R China

It is a big challenge for multi-objective evolutionary algorithms to solve expensive multi-objective optimization due to high computational cost. To effectively address expensive multi-objective optimization, this work proposes a novel surrogate-assisted evolutionary algorithm (SAEA), named bagging-based SAEA (B-SAEA). In the proposed method, bagging is introduced to construct high-quality surrogate ensembles for each expensive objective under a limited number of training points. Thereafter, an evolutionary search is applied to fully search for the constructed surrogate ensembles with the help of generation-based search strategy. Thus, surrogate ensembles and evolutionary search can be seamlessly integrated. In addition, a niche-based infill solutions selection strategy is proposed to select the promising points as the infill solutions for real fitness evaluations. As a result, a good balance between convergence and diversity can be achieved within a limited computational budget. Experimental results on commonly used benchmark test problems and real-world engineering application have demonstrated that the proposed method performs competitively compared with other state-of-the-art methods.

关键词： Expensive multi-objective optimization surrogate-assisted evolutionary algorithm Bagging Niche-based infill solutions selection strategy

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Explicit topology optimization of novel polyline-based core sandwich structures using surrogate-assisted evolutionary algorithm

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020年 369卷 113215-113215页

作者： Chu, Sheng Yang, Zan Xiao, Mi Qiu, Haobo Gao, Kang Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Cardiff Univ Cardiff Sch Engn Queens Bldg14-17 Parade Cardiff CF24 3AA Wales

Inspired by venation patterns of leaves, the new polyline-based core sandwich structures (PCSSs) are developed in this paper. A novel approach is proposed under explicit topology optimization framework based on surrogate-assisted evolutionary algorithm. Specifically, a multi-component topology description function is defined to characterize the shapes of polyline-based cores, and therefore the dimension of design variables in topology optimization of PCSSs is reduced to only tens of dimensions. To solve the optimization problem, a feedback mechanism-driven surrogate-assisted differential evolution algorithm is proposed, where two search phases, i.e., global and local search phases, are respectively implemented in sequence to achieve a good balance between global exploration and local exploitation. Specifically, the global search phase is utilized to not only explore unknown areas for locating the promising regions effectively, but also improve the accuracies of surrogates in specific promising areas. The local search phase is used to intensively search the local promising regions for accelerating the convergence rate. A feedback mechanism is proposed to drive the adaptive search in the global search phase based on three different kinds of feedback information. Based on the superiorities of constraint satisfactions of PCSSs, a volume domination-based epsilon-constraint handling method is employed to handle volume and other constraints differently in an appropriate way. Numerical examples are provided to demonstrate the effectiveness and efficiency of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Polyline-based core sandwich structure Explicit topology optimization surrogate-assisted evolutionary algorithm Feedback mechanism Differential evolution

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A surrogate-assisted evolutionary algorithm for Seeking Multiple Solutions of Expensive Multimodal Optimization Problems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 377-388页

作者： Ji, Jing-Yu Tan, Zusheng Zeng, Sanyou See-To, Eric W. K. Wong, Man-Leung Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China

surrogate-assisted evolutionary algorithms for expensive optimization problems have gained considerable attention in recent years. In many real-world optimization problems, we may face expensive optimization problems with multiple optimal solutions. Locating multiple optima for such expensive problems is qualitatively challenging. This study proposes a surrogate-assisted differential evolution based on region decomposition to seek multiple optima for expensive multimodal optimization problems. In this study, we have designed three major components: 1) the adaptive region decomposition, 2) the multilayer perceptron-based global surrogate, and 3) the self-adaptive gradient descent-based local search. First, the improved adaptive region decomposition detects promising subregions at the beginning phase, and continuously discards inferior subregions successively. Second, the multilayer perceptron-based surrogate and self-adaptive gradient-based mutation work in a collaborative manner on distinct sub-populations to seek multiple optimal solutions. Overall, an attempt has been made to solve expensive multimodal optimization problems. Systematic experiments on 20 test functions show the encouraging and promising performance of our proposed approach.

关键词： Optimization Linear programming Iron Statistics Sociology Multitasking Adaptation models surrogate-assisted evolutionary algorithm expensive multimodal optimization region decomposition multilayer perceptron self-adaptive gradient-based local search

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A surrogate-assisted evolutionary algorithm with clustering-based sampling for high-dimensional expensive blackbox optimization

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JOURNAL OF GLOBAL OPTIMIZATION 2024年第1期89卷 93-115页

作者： Bai, Fusheng Zou, Dongchi Wei, Yutao Chongqing Normal Univ Natl Ctr Appl Math Chongqing Chongqing 401331 Peoples R China

Many practical problems involve the optimization of computationally expensive blackbox functions. The computational cost resulting from expensive function evaluations considerably limits the number of true objective function evaluations allowed in order to find a good solution. In this paper, we propose a clustering-based surrogate-assisted evolutionary algorithm, in which a clustering-based local search technique is embedded into the radial basis function surrogate-assisted evolutionary algorithm framework to obtain sample points which might be close to the local solutions of the actual optimization problem. The algorithm generates sample points cyclically by the clustering-based local search, which takes the cluster centers of the ultimate population obtained by the differential evolution iterations applied to the surrogate model in one cycle as new sample points, and these new sample points are added into the initial population for the differential evolution iterations of the next cycle. In this way the exploration and the exploitation are better balanced during the search process. To verify the effectiveness of the present algorithm, it is compared with four state-of-the-art surrogate-assisted evolutionary algorithms on 24 synthetic test problems and one application problem. Experimental results show that the present algorithm outperforms other algorithms on most synthetic test problems and the application problem.

关键词： Radial basis function Differential evolution surrogate-assisted evolutionary algorithm Clustering High-dimensional expensive blackbox problem

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A Hybrid Between a surrogate-assisted evolutionary algorithm and a Trust Region Method for Constrained Optimization 19

A Hybrid Between a Surrogate-Assisted Evolutionary Algorithm...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Regis, Rommel G. St Josephs Univ Dept Math Philadelphia PA 19131 USA

ISBN: (纸本)9781450367486

This paper develops a hybrid between an RBF-assisted evolutionary Programming (EP) algorithm and the CONORBIT trust region method for constrained expensive black-box optimization. The proposed hybrid combines the advantages of each approach and results in better performance than either the RBF-assisted EP or CONORBIT alone on test problems from the CEC 2010 benchmark.

关键词： Constrained optimization surrogate-assisted evolutionary algorithm trust region hybrid methods radial basis function

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