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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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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 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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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 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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An activity level based surrogate-assisted evolutionary algorithm for many-objective optimization

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APPLIED SOFT COMPUTING 2024年 164卷

作者： Pan, Jeng-Shyang Zhang, An-Ning Chu, Shu-Chu Zhao, Jia Snasel, Vaclav Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

Addressing expensive many-objective optimization problems (MaOPs) is a formidable challenge owing to their intricate objective spaces and high computational demands. surrogate-assisted evolutionary algorithms (SAEAs) have gained prominence because of their ability to tackle MaOPs efficiently. They achieve this by using surrogate models to approximate objective functions, significantly reducing their reliance on costly evaluations. However, the effectiveness of many SAEAs is hampered by their reliance on various surrogate models and optimization strategies, which often result in suboptimal prediction accuracy and optimization performance. This study introduces a novel approach: an activity level based surrogate-assisted reference vector guided evolutionary algorithm specifically designed for expensive MaOPs. Utilizing the Kriging model and an angle penalty distance criterion, this algorithm effectively filters solutions that require evaluation using the original function. It employs a fixed number of training sets,that are updated via a two-screening strategy that leverages activity levels to refine population screening. This process ensures that the reference vector progressively aligns more closely with the Pareto fronts,which is enhanced by the deployment of adjusted adaptive reference vectors, thereby improving the screening precision. The proposed algorithm was tested against six contemporary algorithms using the DTLZ, WFG, and MaF test suites. The experimental results show that the proposed method outperforms other algorithms in most problems. Furthermore, its application to the cloud computing task scheduling problem underscores its practical value, demonstrating its notable effectiveness. The experimental outcomes attest to the robust performance of the algorithm across both test scenarios and real-world applications.

关键词： Expensive many-objective optimization Reference vector Kriging surrogate-assisted evolutionary algorithm Task scheduling

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A surrogate-assisted evolutionary algorithm with dual restricted Boltzmann machines and reinforcement learning-based adaptive strategy selection

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SWARM AND evolutionary COMPUTATION 2024年 89卷

作者： Gong, Yiyun Yu, Haibo Kang, Li Qiao, Gangzhu Guo, Dongpeng Zeng, Jianchao North Univ China Sch Elect & Control Engn Taiyuan 030051 Peoples R China North Univ China Inst Big Data & Visual Comp Taiyuan 030051 Peoples R China North Univ China Sch Environm & Safety Engn Taiyuan 030051 Peoples R China Taiyuan Univ Sci & Technol Sch Environm & Resources Taiyuan 030024 Peoples R China

To improve the effectiveness of surrogate-assisted evolutionary algorithms (SAEAs) in solving high-dimensional expensive optimization problems with multi-polar and multi-variable coupling properties, a new approach called DRBM-ASRL is proposed. This approach leverages restricted Boltzmann machines (RBMs) for feature learning and reinforcement learning for adaptive strategy selection. DRBM-ASRL integrates four search strategies based on three heterogeneous surrogate modeling approaches, each catering to different preferences. Two of these strategies focus on generative sampling in the subspaces with varying dimensions, while the other two aim to explore the local and global landscapes in the high-dimensional source space. This allows for more effective tradeoffs between exploration and exploitation in the solution space. Reinforcement learning is employed to adaptively prioritize the search strategies during optimization , based on the online feedback information from the optimal solution. In addition, to enhance the representation of potentially optimal samples in the solution space, two task-driven RBMs are separately trained to construct a feature subspace and reconstruct the features of the source space. DRBM-ASRL has been evaluated on various high-dimensional benchmarks ranging from 50 to 200 dimensions, as well as 14 CEC 2013 complex benchmark problems with 100 dimensions and a power system problem with 118 dimensions. Experimental results demonstrate its superior convergence performance and optimization efficiency compared to eight state-of-the-art SAEAs.

关键词： High-dimensional expensive problem surrogate-assisted evolutionary algorithm Restricted Boltzmann machines Reinforcement learning Feature reduction and reconstruction

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Decision space partition based surrogate-assisted evolutionary algorithm for expensive optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 214卷

作者： Liu, Yuanchao Liu, Jianchang Tan, Shubin Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

In expensive optimization, function evaluations are based on expensive physical experiments or time consuming simulations. Moreover, the gradient for the objective is not readily available. Therefore, it is a challenge task to deal with expensive optimization. In this work, a decision space partition based surrogate-assisted evolutionary algorithm (DSP-SAEA) is proposed for expensive optimization. In DSP-SAEA, a two-stage search strategy is introduced, where the global search and the local search are seamlessly integrated. In the global search stage, a decision space partition based global search strategy is proposed. In this strategy, all the exactly evaluated points are clustered into a set of clusters. Thus, the decision space can be partitioned into several regions based on the formed clusters. Furthermore, in each region, the surrogate model is constructed. The algorithm will search for these regions simultaneously with the help of the built surrogate models. As a result, several promising points distributed in different regions are able to be obtained. In the local search stage, a model adaptive selection strategy and the trust region local search are integrated. The model adaptive selection strategy is introduced to accurately assist the trust region local search, where the local elite surrogate model is adaptively chosen from the local surrogate model pool. Experimental results on benchmark problems and the parameter estimation for frequency-modulated sound waves problem demonstrate that DSP-SAEA performs competitively compared with some state-of-the-art algorithms.

关键词： Decision space partition Model adaptive selection surrogate-assisted evolutionary algorithm Two-stage search Expensive optimization

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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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