Dual-Surrogate-Assisted Cooperative Particle Swarm Optimization for Expensive Multimodal Problems

Various real-world applications can be classified as expensive multimodal optimization problems. When surrogate-assisted evolutionary algorithms (SAEAs) are employed to tackle these problems, they not only face a contradiction between the precision of surrogate models and the cost of individual eval...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2021-08, Vol.25 (4), p.794-808
Main Authors: Ji, Xinfang, Zhang, Yong, Gong, Dunwei, Sun, Xiaoyan
Format: Article
Language:English
Subjects:
Clustering
Clustering algorithms
Coevolution
Computing costs
Evaluation
Evolutionary algorithms
expensive optimization
Genetic algorithms
multimodal
Optimization
Particle swarm optimization
particle swarm optimization (PSO)
Prediction algorithms
Sociology
Statistics
Sun
surrogate-assisted
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Summary:Various real-world applications can be classified as expensive multimodal optimization problems. When surrogate-assisted evolutionary algorithms (SAEAs) are employed to tackle these problems, they not only face a contradiction between the precision of surrogate models and the cost of individual evaluations but also have the difficulty that surrogate models and problem modalities are hard to match. To address this issue, this article studies a dual-surrogate-assisted cooperative particle swarm optimization algorithm to seek multiple optimal solutions. A dual-population cooperative particle swarm optimizer is first developed to simultaneously explore/exploit multiple modalities. Following that, a modal-guided dual-layer cooperative surrogate model, which contains one upper global surrogate model and a group of lower local surrogate models, is constructed with the purpose of reducing the individual evaluation cost. Moreover, a hybrid strategy based on clustering and peak-valley is proposed to detect new modalities. Compared with five existing SAEAs and seven multimodal evolutionary algorithms, the proposed algorithm can simultaneously obtain multiple highly competitive optimal solutions at a low computational cost according to the experimental results of testing both 11 benchmark instances and the building energy conservation problem.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2021.3064835