粒子群最適化アルゴリズムのパラメータ選択と拡張

カテゴリ: 論文誌(論文単位)

グループ名: 【A】基礎・材料・共通部門

発行日: 2011/07/01

タイトル(英語): Parameter Selection and Extension of Particle Swarm Optimization Algorithm

著者名: 孟　志奇（福岡大学）

著者名(英語): Zhiqi Meng (Fukuoka University)

キーワード: 粒子群最適化，パラメータ選択，探索性能，局所解，粒子リフレッシュテクニック，引力圏認識アルゴリズム　　particle swarm optimization(PSO)，parameter selection，searching-performance，local minimum，particle refresh technique，attraction basin recognition algorithm

要約(英語): Particle swarm optimization (PSO) is a powerful tool for designing antennas, solving inverse scattering problems, and so on. The algorithm of PSO is controlled with several parameters. Unless the parameters are selected appropriately, the search efficiency of PSO drops significantly. There are, however, no clear rules for the selection, and users have considerable difficulty to use PSO efficiently. This paper proposes a guideline and a new technique "particle refresh" for the selection to make the algorithm easy-to-use and keeping high searching-performance. The hybridization between PSO and conjugate gradient method is also discussed to utilize their complementary advantages in global exploration and local exploitation, where "attraction basin recognition" algorithm is proposed to recognizing the attraction basin area of local minima and help the algorithm to escape from local minima certainly and efficiently.

本誌: 電気学会論文誌A（基礎・材料・共通部門誌） Vol.131 No.7 （2011） 特集：高周波マイクロ磁気応用技術の最前線

本誌掲載ページ: 529-539 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejfms/131/7/131_7_529/_article/-char/ja/