分類モデルと近似モデルを併用したハイブリッドサロゲート粒子群最適化法
書誌事項
- タイトル別名
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- Hybrid Surrogate-Assisted Particle Swarm Optimization Based on Approximation and Classification Models
抄録
<p>Surrogate-assisted PSOs are one of the most popular black-box optimizers for computationally expensive optimization problems, but those employ approximation models less scalable for the increase of the problem dimension on a restricted number of fitness evaluations. This paper proposes a hybrid surrogate-assisted PSO (HyPSO), which utilizes approximation and classification surrogate models for computationally expensive optimization problems. A basic idea of HyPSO is in the hybridization of surrogate model types, although existing works only consider the approximation model compatible with the PSO framework. HyPSO intends to manage a trade-off between approximation/classification models in terms of the model accuracy and the screening capacity; this contributes to hedge the risk of the over-fitting issue in building surrogates under a restriction of fitness evaluations. In particular, HyPSO constructs an approximation model with Radial Basis Function (RBF) and a classification model with Support Vector Machine (SVM). Then, it estimates a global best solution and a personal-best solution of a particle with an RBF model and an SVM model, respectively. Experimental results show that HyPSO significantly outperforms an alternative approach, i.e., OPUS and the standard PSO on a set of single-objective benchmark functions. Especially, HyPSO has a good scalability against the increase of the problem dimension.</p>
収録刊行物
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- 進化計算学会論文誌
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進化計算学会論文誌 12 (3), 73-87, 2021
進化計算学会
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詳細情報 詳細情報について
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- CRID
- 1390290769928302464
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- NII論文ID
- 130008141288
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- ISSN
- 21857385
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可