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Multifidelity surrogates-assisted multi-objective particle swarm algorithm for offline data-driven optimization

APPLIED INTELLIGENCE 2024年第22期54卷 11649-11671页

作者： Cui, Yingying Meng, Xi Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Intelligent Percept & Autonomous Cont Beijing 100124 Peoples R China

Surrogate-assisted evolutionary algorithms have been widely employed to solve data-driven optimization problems. However, for offline data-driven optimization, it is very challenging to perform evolutionary search efficiently as well as accurately since no new data is available during the optimization process. To mitigate this issue, a multifidelity surrogates-assisted multi-objective particle swarm optimization (MFSa-PSO) algorithm is proposed in this paper. First, two low-fidelity models with convergence and diversity characteristics separately and a high-fidelity model are constructed to assemble multifidelity surrogate models. Second, by adopting the knowledge transfer strategy, the multifidelity surrogates-assisted two-archive multi-objective particle swarm optimization is conducted to search optimal solutions more exactly and effectively. Third, the output solution set is achieved by associating the solutions of two archives with reference vectors. Finally, the proposed MFSa-PSO is compared with some popular surrogate-assisted evolutionary algorithms on benchmark problems to verify its effectiveness and outperformance. Additionally, a real-world application of the municipal solid waste incineration process is carried out to verify the engineering applicability of MFSa-PSO.

关键词： offline data-driven optimization Multifidelity surrogate models Knowledge transfer Two-archive multi-objective particle swarm optimization Reference vectors

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offline data-driven Evolutionary optimization Using Selective Surrogate Ensembles

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2019年第2期23卷 203-216页

作者： Wang, Handing Jin, Yaochu Sun, Chaoli Doherty, John Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Liaoning Peoples R China Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Univ Surrey Dept Mech Engn Sci Guildford GU2 7XH Surrey England

In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions. Instead, surrogate models must be built based on historical data to approximate the objective functions and no new data will be available during the optimization process. Such problems are known as offline data-driven optimization problems. Since the surrogate models solely depend on the given historical data, the optimization algorithm is able to search only in a very limited decision space during offline data-driven optimization. This paper proposes a new offline data-driven evolutionary algorithm to make the full use of the offline data to guide the search. To this end, a surrogate management strategy based on ensemble learning techniques developed in machine learning is adopted, which builds a large number of surrogate models before optimization and adaptively selects a small yet diverse subset of them during the optimization to achieve the best local approximation accuracy and reduce the computational complexity. Our experimental results on the benchmark problems and a transonic airfoil design example show that the proposed algorithm is able to handle offline data-driven optimization problems with up to 100 decision variables.

关键词： Ensemble evolutionary algorithm (EA) offline data-driven optimization radial basis function networks (RBFNs) surrogate

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offline data-driven Multiobjective optimization: Knowledge Transfer Between Surrogates and Generation of Final Solutions

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第3期24卷 409-423页

作者： Yang, Cuie Ding, Jinliang Jin, Yaochu Chai, Tianyou Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

In offline data-driven optimization, only historical data is available for optimization, making it impossible to validate the obtained solutions during the optimization. To address these difficulties, this paper proposes an evolutionary algorithm assisted by two surrogates, one coarse model and one fine model. The coarse surrogate (CS) aims to guide the algorithm to quickly find a promising subregion in the search space, whereas the fine one focuses on leveraging good solutions according to the knowledge transferred from the CS. Since the obtained Pareto optimal solutions have not been validated using the real fitness function, a technique for generating the final optimal solutions is suggested. All achieved solutions during the whole optimization process are grouped into a number of clusters according to a set of reference vectors. Then, the solutions in each cluster are averaged and outputted as the final solution of that cluster. The proposed algorithm is compared with its three variants and two state-of-the-art offline data-driven multiobjective algorithms on eight benchmark problems to demonstrate its effectiveness. Finally, the proposed algorithm is successfully applied to an operational indices optimization problem in beneficiation processes.

关键词： optimization Knowledge transfer Task analysis data models Sociology Statistics Evolutionary computation Knowledge transfer multiobjective evolutionary algorithms (MOEAs) multisurrogate offline data-driven optimization

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offline data-driven Evolutionary optimization Algorithm Using K-Fold Cross 13th

Offline Data-Driven Evolutionary Optimization Algorithm Usin...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Wang, Mengzhen Shan, Yawen Xu, Fei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

In the field of science and engineering, there are many offline data-driven optimization problems, which have no mathematical functions, and cannot use numerical simulations or physical experiments, but can only use the historical data collected in ordinary times to evaluate the quality of candidate solutions during the optimization process. In order to solve offline data-driven optimization problems, offline data-driven evolutionary algorithms use historical data to build surrogate models to simulate the real objective function. In this paper, an offline data-driven evolutionary optimization algorithm using k-fold cross is proposed. The proposed algorithm uses radial basis function networks as surrogate models and uses the k-fold cross method to build the ensemble surrogate in order to reduce the number of surrogate models in the surrogate and the time cost of the algorithm. To improve the performance of the algorithm, the number of hidden layer neurons and the kernel function in radial basis function network are determined by analyzing the effects of the parameters on the performance of the algorithm. Experimental results on benchmark problems show that the algorithm has good performance and low time cost. Moreover, a similar algorithm uses the parameters of radial basis function network in the proposed algorithm, the performance of the algorithm is improved, which indicates that the parameters have some universality.

关键词： offline data-driven optimization Surrogate model Evolutionary algorithm Radial-basis-function network K-fold cross-validation

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offline data-driven Particle Swarm optimization Assisted by Selective Surrogate Ensembles and Hybrid Search Strategies 34

Offline Data-Driven Particle Swarm Optimization Assisted by ...

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34th Chinese Control and Decision Conference (CCDC)

作者： Wei, Jiamin Niu, Haoyu Xidian Univ Sch Telecommun Engn State Key Lab ISN Xian 710071 Peoples R China Univ Calif Merced Sch Engn Merced CA 95343 USA

ISBN: (纸本)9781665478960

Real-world optimization problems usually involve the case of offline data-driven optimization, where any real fitness evaluations are not allowed during the optimization. Although surrogate models can be used for fitness approximation, it is still a grand challenge for applying evolutionary algorithms to solve offline data-driven optimization problems. To address this issue, a new algorithm, called DDmPSO-SE, is presented in this paper. In the proposed approach, both the surrogate ensemble and local surrogate are employed to fully exploit the useful information of the offline data and the entire population. Besides, hybrid search strategies are designed to improve the search efficiency of the swarm, and a simple yet effective replacement method is put forward to enhance the optimization performance further. The experimental results indicate that DDmPSO-SE is a promising method for handling offline data-driven optimization problems and achieves competitive results compared to other offline algorithms.

关键词： offline data-driven optimization Surrogate Model Particle Swarm optimization Evolutionary Algorithm

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offline data-driven Particle Swarm optimization Assisted by Selective Surrogate Ensembles and Hybrid Search Strategies

Offline Data-Driven Particle Swarm Optimization Assisted by ...

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第34届中国控制与决策会议

作者： Jiamin Wei Haoyu Niu State Key Laboratory of ISN School of Telecommunications EngineeringXidian University School of Engineering University of California

Real-world optimization problems usually involve the case of offline data-driven optimization,where any real fitness evaluations are not allowed during the *** surrogate models can be used for fitness approximation,it is still a grand challenge for applying evolutionary algorithms to solve offline data-driven optimization *** address this issue,a new algorithm,called DDmPSO-SE,is presented in this *** the proposed approach,both the surrogate ensemble and local surrogate are employed to fully exploit the useful information of the offline data and the entire ***,hybrid search strategies are designed to improve the search efficiency of the swarm,and a simple yet effective replacement method is put forward to enhance the optimization performance *** experimental results indicate that DDmPSO-SE is a promising method for handling offline data-driven optimization problems and achieves competitive results compared to other offline algorithms.

关键词： offline data-driven optimization Surrogate Model Particle Swarm optimization Evolutionary Algorithm

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A Gaussian Process Assisted offline Estimation of Multivariate Gaussian Distribution Algorithm

A Gaussian Process Assisted Offline Estimation of Multivaria...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ma, Xin-Xin Chen, Wei-Neng Yang, Qiang South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728185262

Surrogated assisted evolutionary algorithms are commonly used to solve real-world expensive optimization problems. However, in some situations, no online data is available during the evolution process. In this situation, we have to build surrogate models based on offline historical data, which is known as offline data-driven optimization. Since no new data can be used to improve the surrogate models, offline data-driven optimization remains a challenging problem. In this paper, we propose a Gaussian process assisted offline estimation of multivariate Gaussian distribution algorithm to address the offline data-driven optimization problem. Instead of using surrogate models to predict the fitness values of individuals, we utilize a surrogate model to predict the rankings of individuals based on the frequently used lower confidence bound. In this way, the robustness of the proposed algorithm could be enhanced. Experiments are conducted on five commonly used benchmark problems. The experimental results demonstrate that the proposed offline surrogate model and the multivariate Gaussian estimation of distribution algorithm are able to achieve competitive performance.

关键词： offline data-driven optimization surrogate models Gaussian Regression Process multivariate Gaussian distribution estimation of distribution algorithms

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