Performance Improvement of Element Description Method Using Artificial Bee Colony Algorithm

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門（英文）

発行日: 2022/09/01

タイトル(英語): Performance Improvement of Element Description Method Using Artificial Bee Colony Algorithm

著者名: Issei Takeuchi (Advanced Technology Research Div, Research and Development Dept., Tokyo Automatic Machinery Works, Ltd.), Seiichiro Katsura (Department of System Design Engineering, Keio University)

著者名(英語): Issei Takeuchi (Advanced Technology Research Div, Research and Development Dept., Tokyo Automatic Machinery Works, Ltd.), Seiichiro Katsura (Department of System Design Engineering, Keio University)

キーワード: artificial bee colony algorithm，element description method，genetic algorithm，particle swarm optimization，system identification，temperature control

要約(英語): In order to improve control performance in various control fields, it is important to model the controlled object accurately. In this case, the quality of the model is considerably influenced by the structure of the model determined by the engineer. An element description method is a method that can optimize not only parameters but also the structure of the model. Therefore, it is possible to search over a wide range without being restricted by human design. However, this considerably increases the search space, and it is easy to fall into a local solution. In this study, the artificial bee colony algorithm is combined with the element description method to improve its search ability. The artificial bee colony algorithm is known to be effective for high-dimensional and multimodal problems. The performance of the proposed method is validated using a heat sealing system in packaging machinery. The proposed method is evaluated in comparison with the genetic algorithm, which is a conventional method. Experiments confirm that the local solution avoidance performance of the artificial bee colony algorithm is significantly better than that of the genetic algorithm.

本誌: IEEJ Journal of Industry Applications Vol.11 No.5 （2022）

本誌掲載ページ: 643-649 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/11/5/11_21005358/_article/-char/ja/