Surrogate and Autoencoder-Assisted Multitask Particle Swarm Optimization for High-Dimensional Expensive Multimodal Problems

Ji, Xinfang; Zhang, Yong; He, Chuangxin; Cheng, Jinxin; Gong, Dunwei; Gao, Xiao‐Zhi; Guo, Yinan

doi:10.1109/tevc.2023.3287213

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…Two swarms are respectively used in different optimization states. Xin et al [42] proposed a surrogate and autoencoder-assisted multitask particle swarm optimization algorithm to solve multimodal optimization problems. A surrogate-assisted differential evolution with knowledge transfer (SADE-KT) [20] integrate knowledge transfer and the surrogate-assisted evolutionary search proposed for expensive incremental optimization problems.…”

Section: Related Workmentioning

confidence: 99%

A Multi-level Surrogate-assisted Algorithm for Expensive Optimization Problems

Hu,

Wu,

Che

2024

ITC

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With the development of computer science, more and more complex problems rely on the help of computers for solving. When facing the parameter optimization problem of complex models, traditional intelligent optimization algorithms often require multiple iterations on the target problem. It can bring unacceptable costs and resource costs in dealing with these complex problems. In order to solve the parameter optimization of complex problems, in this paper we propose a multi-level surrogate-assisted optimization algorithm (MLSAO). By constructing surrogate models at different levels, the algorithm effectively explores the parameter space, avoiding local optima and enhancing optimization efficiency. The method combines two optimization algorithms, differential evolution (DE) and Downhill simplex method. DE is focused on global level surrogate model optimization. Downhill simplex is concentrated on local level surrogate model update. Random forest and inverse distance weighting (IDW) are constructed for global and local level surrogate model respectively. These methods leverage their respective advantages at different stages of the algorithm. The MLSAO algorithm is evaluated against other state-of-the-art approaches using benchmark functions of varying dimensions. Comprehensive results from the comparisons showcase the superior performance of the MLSAO algorithm in addressing expensive optimization problems. Moreover, we implement the MLSAO algorithm for tuning precipitation parameters in the Community Earth System Model (CESM). The outcomes reveal its effective enhancement of CESM's simulation accuracy for precipitation in the North Indian Ocean and the North Pacific region. These experiments demonstrate that MLSAO can better address parameter optimization problems under complex conditions.

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Section: Related Workmentioning

confidence: 99%

A Multi-level Surrogate-assisted Algorithm for Expensive Optimization Problems

Hu,

Wu,

Che

2024

ITC

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Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains

Liu

Nishi

2023

Complex Intell. Syst.

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Supply chain digital twin has emerged as a powerful tool in studying the behavior of an actual supply chain. However, most studies in the field of supply chain digital twin have only focused on what-if analysis that compares several different scenarios. This study proposes a data-driven evolutionary algorithm to efficiently solve the service constrained inventory optimization problem using historical data that generated by supply chain digital twins. The objective is to minimize the total costs while satisfying the required service level for a supply chain. The random forest algorithm is used to build surrogate models which can be used to estimate the total costs and service level in a supply chain. The surrogate models are optimized by an ensemble approach-based differential evolution algorithm which can adaptively use different search strategies to improve the performance during the computation process. A three-echelon supply chain digital twin on the geographic information system (GIS) map in real-time is used to examine the efficiency of the proposed method. The experimental results indicate that the data-driven evolutionary algorithm can reduce the total costs and maintain the required service level. The finding suggests that our proposed method can learn from the historical data and generate better inventory policies for a supply chain digital twin.

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation

Wu,

Liu,

et al. 2024

2024 IEEE Congress on Evolutionary Computation (CEC)

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Surrogate and Autoencoder-Assisted Multitask Particle Swarm Optimization for High-Dimensional Expensive Multimodal Problems

Cited by 4 publications

References 43 publications

A Multi-level Surrogate-assisted Algorithm for Expensive Optimization Problems

A Multi-level Surrogate-assisted Algorithm for Expensive Optimization Problems

Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation

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