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A surrogate-assisted evolutionary algorithm with hypervolume triggered fidelity adjustment for noisy multiobjective integer programming

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APPLIED SOFT COMPUTING 2022年 126卷

作者： Liu, Shulei Wang, Handing Yao, Wen Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Chinese Acad Mil Sci Def Innovat Inst Beijing 100071 Peoples R China

Although surrogate-assisted evolutionary algorithms (SAEAs) have been widely developed to address computationally expensive multi-objective optimization problems (MOPs), they still encounter dif-ficulties in solving the expensive and noisy combinatorial MOPs. To this end, we propose a novel SAEA to handle this kind of problem. In the proposed algorithm, the averaging method is used to denoise. To balance the conflict between the time cost and the effect of noises, multi-fidelity surrogate models are constructed according to the averaged evaluation results. The number of independent repeated evaluations represents the fidelity level of surrogate models. In the optimization process, the hypervolume indicator is employed as a trigger to determine whether the fidelity level should be increased. In addition, a lightweight local search method, the semi-variable neighborhood search, is proposed to improve the global search efficiency of the proposed algorithm in discrete decision spaces. Experimental results show that our proposed algorithm achieves competitive performance on most benchmark problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Noisy multiobjective integer programming Multi -fidelity optimization Fidelity adjustment strategy surrogate-assisted evolutionary algorithm

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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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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 dual surrogate assisted evolutionary algorithm based on parallel search for expensive multi/many-objective optimization

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Shen, Jiangtao Wang, Peng Tian, Ye Dong, Huachao Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230610 Peoples R China

Numerous optimization problems in the real world involve multi-objective and computationally expensive simulations (i.e., expensive multi-objective optimization problems). This paper purposes a dual surrogates-assisted evolutionary algorithm (SAEA) based on parallel search, termed DSAEA-PS, for this issue. Approximation and classification are two main implementation forms of surrogate models, but the existing methods of expensive multi-objective optimization only apply one kind of them, and scarce works have paid attention to combining approximation and classification to improve the optimization performance. In the proposed algorithm, to enhance the prediction accuracy and reliability, both the approximation model and classification model are applied to cooperate to provide the quality and uncertainty information of candidate solutions. Meanwhile, the parallel search based on heterogeneous multi-objective evolutionary algorithms is introduced for better exploration of the decision space. In addition, combined with the strengthened dominance relation (SDR), a sampling strategy that comprehensively considers the quality of candidate solutions and their uncertainty information is proposed. Experimental results with five peer competitors on a set of widely-used benchmark problems demonstrate the ability of DSAEA-PS. Furthermore, DSAEA-PS is adopted for a five-objective blended wing-body underwater glider design problem that involves time-consuming simulations of fluid dynamics and structural strength. A series of high-performance solutions obtained from DSAEA-PS verifies its effectiveness on engineering applications.

关键词： surrogate-assisted evolutionary algorithm Expensive multi-objective optimization Approximation and classification Parallel search Strengthened dominance relation

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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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Predictability on Performance of surrogate-assisted evolutionary algorithm According to Problem Dimension 19

Predictability on Performance of Surrogate-assisted Evolutio...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Yu, Dong-Pil Kim, Yong-Hyuk Kwangwoon Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781450367486

As the demand for computationally expensive optimization has increased, so has the interest in surrogate-assisted evolutionary algorithms (SAEAs). However, if a fitness landscape is approximated using only a surrogate model, thereby replacing a fitness function, it is possible for a solution to evolve toward a false optimum based on the surrogate model. Therefore, many conventional studies have been carried out in which the real fitness function and surrogate model are dealt with simultaneously. Nevertheless, such an approach leaves much to be desired because studies should be performed for real fitness function evaluation and surrogate model-aware search mechanisms. In this study, we discovered that the approximation error of the surrogate model at low dimensions has a significant relationship with the performance of SAEAs at high dimensions for three binary encoding problems and three real encoding problems. Therefore, if the approximate error is sufficiently small in the low dimension, then high GA performance can be obtained even when the real fitness function is not used, because a high-quality surrogate model can be obtained in the high dimension.

关键词： surrogate-assisted evolutionary algorithm genetic algorithm machine learning

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A multiple surrogate-assisted hybrid evolutionary feature selection algorithm

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Zhang, Wan-qiu Hu, Ying Zhang, Yong Zheng, Zi-wang Peng, Chao Song, Xianfang Gong, Dunwei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Anhui Normal Univ Sch Comp & Informat Wuhu 241000 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266100 Peoples R China

Feature selection (FS) is an important data processing technology. However, existing FS methods based on evolutionary computation have still the problems of "curse of dimensionality"and high computational cost, with the increase of the number of feature and/or the size of instance. In view of this, the paper proposes a multiple surrogate-assisted hybrid evolutionary feature selection (MSa-HEFS). Two kinds of surrogates (i.e., objective regression surrogate and sample surrogate) and two kinds of FS methods (i.e., filter and wrapper) are integrated into MSa-HEFS to improve its performance. Firstly, an ensemble filter FS method is designed to reduce the search space of subsequent wrapper evolutionary FS method. Secondly, in the proposed evolutionary FS method, a dual-surrogate-assisted hierarchical individual evaluation mechanism is developed to reduce the evaluation cost on feature subsets, an online management and update strategy is used to adaptively choose appropriate surrogates for individuals. The proposed algorithm is applied to 12 typical datasets and compared with 4 state-of-the-art FS algorithms. Experimental results show that MSa-HEFS can obtain good feature subsets at the smallest computational cost on all datasets. MSa-HEFS source code is available on Github at https://***/ZZW-zq/MSa-HEFS-/tree/master.

关键词： Brain storming optimization Swarm intelligence optimization algorithm Feature selection surrogate-assisted evolutionary algorithm

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A local correlation estimation surrogate-assisted bi-objective evolutionary algorithm for heterogeneous objectives

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Gu, Chenyan Wang, Handing Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China

Most existing evolutionary algorithms for solving bi-objective optimization problems are based on the assumption that both objectives can be evaluated once at the same time. However, in real-world optimization problems, there is a significant difference in function evaluation time between objectives, and these problems are defined as the problems with heterogeneous objectives. To utilize the latency of objectives, we propose a local correlation estimation based surrogate-assisted bi-objective evolutionary algorithm for problems with heterogeneous objectives. In the proposed algorithm, surrogate models are employed to approximate the objective functions. The proposed local correlation estimation (LCE) method is used to analyze the correlation between objectives within a local region, guiding the search direction for one objective while identifying promising solutions for the other objective. Finally, the ablation experiment for the LCE method validates the effectiveness of the proposed strategy, and the comparative results on various expensive bi-objective test problems demonstrate that the proposed algorithm is promising on heterogeneous bi-objective optimization problems.

关键词： Bi-objective optimization Non-uniform evaluation times surrogate models Gaussian process surrogate-assisted evolutionary algorithm Bayesian optimization

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RM-SAEA: Regularity Model Based surrogate-assisted evolutionary algorithms for Expensive Multi-Objective Optimization 23

RM-SAEA: Regularity Model Based Surrogate-Assisted Evolution...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Lu, Yongfan Li, Bingdong Qian, Hong Hong, Wenjing Yang, Peng Zhou, Aimin East China Normal Univ Shanghai Inst AI Educ Shanghai Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen Peoples R China

ISBN: (纸本)9798400701191

Due to computationally and/or financially costly evaluation, tackling expensive multi-objective optimization problems is quite challenging for evolutionary algorithms. One popular approach to these problems is building cheap surrogate models to replace the expensive real function evaluations. To this end, various kinds of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed, building surrogate models which predict the fitness values, classifications, or relation of the candidate solutions. However, offspring generation, despite its important role in evolutionary optimization, has not received enough attention in these SAEAs. In this paper, a regularity model based framework, namely RM-SAEA, is proposed for better offspring generation in expensive multi-objective optimization. To be specific, RM-SAEA is featured with a heterogeneous offspring generation module, which is composed of a regularity model and a general genetic operator. Moreover, in order to alleviate the data deficiency issue in the expensive optimization scenario, a data augmentation strategy is employed while training the regularity model. Finally, two representative SAEAs are embedded into RM-SAEA in order to instantiate the proposed framework. Experimental results on benchmark multi-objective problems with up to 10 objectives demonstrate that RM-SAEA achieves the best overall performance compared with 6 state-of-the-art algorithms.

关键词： expensive multi-objective optimization regularity model Pareto set learning surrogate-assisted evolutionary algorithm

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Utilizing the Expected Gradient in surrogate-assisted evolutionary algorithms

Utilizing the Expected Gradient in Surrogate-assisted Evolut...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Nishihara, Kei Nakata, Masaya Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9798400701207

In the field of surrogate-assisted evolutionary algorithms (SAEAs), Gaussian Process (GP) is a widely used technique to approximate the objective function. Although a GP model can provide an expected gradient of a function to be approximated, little attention has been paid to the utilization of the gradient information. Thus, this paper presents an expected gradient-based SAEA, in which the expected gradient of the objective function provided by the GP models is utilized to conduct an efficient local search. Specifically, the proposed algorithm first conducts a global search with a differential evolution algorithm to find promising regions of the search space. Then, it builds a GP model for each promising region, and a quasi-Newton method (L-BFGS-B) is executed on its model with guidance from the expected gradient. This gradient-based local search intends to sufficiently search the approximate objective function, by finding various local optimal solutions in an efficient manner. Experimental results show that our algorithm is competitive with state-of-the-art SAEAs on a single-objective optimization benchmark suite.

关键词： surrogate-assisted evolutionary algorithm expected gradient quasi-Newton method differential evolution Gaussian process

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