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

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 optimizati...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 22; pp. 11649 - 11671
Main Authors: Cui, Yingying, Meng, Xi, Qiao, Junfei
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2024
Springer Nature B.V
Subjects:
Accuracy
Archives & records
Artificial Intelligence
Computer Science
Data search
Evolutionary algorithms
Genetic algorithms
Knowledge management
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Municipal waste management
Optimization
Particle swarm optimization
Processes
Solid waste management
Solid wastes
Waste disposal
Two-archive multi-objective particle swarm optimization
Offline data-driven optimization
Multifidelity surrogate models
Knowledge transfer
Reference vectors
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary: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. Graphical Abstract
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-05612-w