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2010 IEEE World Congress on Computational Intelligence

作者： Zhou, Aimin Zhang, Qingfu East China Normal Univ Dept Comp Sci & Technol Shanghai 200062 Peoples R China Univ Essex Sch Elect Engn & Comp Sci Colchester CO4 3SQ Essex England

ISBN: (纸本)9781424481262

Minimax optimization requires to minimize the maximum output in all possible scenarios. It is a very challenging problem to evolutionary computation. In this paper, we propose a surrogate-assisted evolutionary algorithm, Minimax SAEA, for tackling minimax optimization problems. In Minimax SAEA, a surrogate model based on Gaussian process is built to approximate the mapping between the decision variables and the objective value. In each generation, most of the new solutions are evaluated based on the surrogate model and only the best one is evaluated by the actual objective function. Minimax SAEA is tested on six benchmark problems and the experimental results show that Minimax SAEA can successfully solve five of them within 110 function evaluations.

关键词： Gaussian process Gaussian processes decision variables evolutionary computation evolutionary computation minimax optimization problem minimax techniques objective function objective value surrogate-assisted evolutionary algorithm

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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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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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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 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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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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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 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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AIEA: An Asynchronous Influence-Based evolutionary algorithm for Expensive Many-Objective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第2期55卷 786-799页

作者： Wei, Feng-Feng Chen, Wei-Neng Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Buildingand Urban Sci Guangzhou 510006 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 30071 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea

In expensive multi/many-objective optimization problems (EMOPs), the expensive objectives are generally accessed through different simulation tools, leading to different evaluation latencies and unbearable computational time for serial optimization. One promising approach to improve efficiency is to perform simulation and build surrogates separately for each objective in parallel. However, how to improve the model accuracy and select promising candidates without global information are big challenges. To alleviate these problems, this article proposes an asynchronous influence-based SAEA (AIEA) based on the client-server model. Each client approximates an objective and the server takes charge for evolution. To adaptively select promising candidates, the influence degree is introduced in candidate selection, which is calculated in the objective space to judge which candidate has more beneficial influence for evolution. With the selected candidate, the most-uncertain-first strategy is devised in objective selection for asynchronous evaluations and model improvement. To handle incomplete objective values, the nearest neighbor inheritance is adopted for unevaluated objectives. Comprehensive experiments compared with five surrogate-assisted EAs demonstrate the global optimization and scalability of AIEA.

关键词： Optimization Computational modeling Predictive models Training data Data models Vectors Servers Program processors evolutionary computation Distributed databases Expensive optimization many-objective optimization problems parallel and distributed evolutionary computation surrogate-assisted evolutionary algorithm

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Machine-Learning-assisted Swarm Intelligence algorithm for Antenna Optimization With Mixed Continuous and Binary Variables

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第3期73卷 1662-1673页

作者： Fu, Kai Leung, Kwok Wa City Univ Hong Kong State Key Lab Terahertz & Millimeter Wave Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China CityU Shenzhen Res Inst Informat & Commun Technol Ctr Shenzhen 518057 Peoples R China

Many existing surrogate-assisted optimization algorithms are limited to designing antennas with continuous variables only. However, numerous challenges emerge when tackling antenna optimization problems that involve both continuous and binary design variables. This article proposes an efficient surrogate-assisted mixed continuous/binary particle swarm optimization (SAMPSO) algorithm to address these mixed-variable antenna optimization problems. The SAMPSO tightly integrates machine learning (ML) models with PSO in two key aspects: an ML-guided swarm updating method and an ML-assisted prescreening strategy. In addition, a novel local ML model training method is developed to reduce the algorithm time complexity. To verify its effectiveness, the SAMPSO is compared with two existing algorithms in solving benchmark functions and designing antennas. The results demonstrate that SAMPSO can achieve design objectives with a faster convergence speed.

关键词： Optimization Antennas Vectors Computational modeling Particle swarm optimization Heuristic algorithms Optimization methods Accuracy Training Partitioning algorithms Antenna optimization mixed-variable optimization particle swarm optimization (PSO) surrogate-assisted evolutionary algorithm

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