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| 講演名 | 2020-05-15 13:50 Spy Algorithm for Solving Continuous Optimization Problem ○Dhidhi Pambudi・Masaki Kawamura（Yamaguchi Univ.）  NLP2020-5 |
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| 抄録 | （和） | We proposed the Spy algorithm for solving continuous optimization problems on multimodal functions. The algorithm is one of population-based metaheuristics and is inspired by spy-ring (a cooperated group of spies). The multimodal function may have many local optimums and some of them can be close to the global optimum. Some optimization methods can be trapped in these local optimum and fail to reach the global optimum. Moreover, multimodal function can have many flat plateaus and valleys that make it much harder to solve. The Spy algorithm accommodates exploitation and exploration search in a simple concept and simple operation that make it easy for tuning to get a good result. In order to check the performance of the proposed method, we applied it to some benchmarking tests, which are Ackley, Schwefel, Rastrigin, Dixon-Price, and Michalewicz function on dimension size 3, 5, 10, 15, and 20. We compare the Spy algorithm against Cuckoo Search with and without Levy flight and FFA. Based on the result, the average of error on Ackley, Dixon-Price, and Michalewicz function regardless of dimension size for Spy Algorithm, CS non-Levy, CS Levy, and FFA are 0.48, 18.12, 24.79, and 67921.71. All algorithm was weak on Rastrigin function and very weak on Schwefel function, but the Spy Algorithm still overcame all other compared algorithm. The average computation time regardless of the test functions on all dimension size for Spy Algorithm, CS non-Levy, CS Levy, and FFA are 0.46s, 0.23s, 1.02s, and 5.09s. The Spy algorithm was fast enough and had the best accuracy. |
| （英） | We proposed the Spy algorithm for solving continuous optimization problems on multimodal functions. The algorithm is one of population-based metaheuristics and is inspired by spy-ring (a cooperated group of spies). The multimodal function may have many local optimums and some of them can be close to the global optimum. Some optimization methods can be trapped in these local optimum and fail to reach the global optimum. Moreover, multimodal function can have many flat plateaus and valleys that make it much harder to solve. The Spy algorithm accommodates exploitation and exploration search in a simple concept and simple operation that make it easy for tuning to get a good result. In order to check the performance of the proposed method, we applied it to some benchmarking tests, which are Ackley, Schwefel, Rastrigin, Dixon-Price, and Michalewicz function on dimension size 3, 5, 10, 15, and 20. We compare the Spy algorithm against Cuckoo Search with and without Levy flight and FFA. Based on the result, the average of error on Ackley, Dixon-Price, and Michalewicz function regardless of dimension size for Spy Algorithm, CS non-Levy, CS Levy, and FFA are 0.48, 18.12, 24.79, and 67921.71. All algorithm was weak on Rastrigin function and very weak on Schwefel function, but the Spy Algorithm still overcame all other compared algorithm. The average computation time regardless of the test functions on all dimension size for Spy Algorithm, CS non-Levy, CS Levy, and FFA are 0.46s, 0.23s, 1.02s, and 5.09s. The Spy algorithm was fast enough and had the best accuracy. | |
| キーワード | （和） | optimization / continuous / metaheuristic / / / / / |
| （英） | optimization / continuous / metaheuristic / / / / / | |
| 文献情報 | 信学技報, vol. 120, no. 26, NLP2020-5, pp. 23-28, 2020年5月. | |
| 資料番号 | NLP2020-5 | |
| 発行日 | 2020-05-08 (NLP) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | NLP2020-5 |
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| 研究会情報 | |
| 研究会 | NLP |
| 開催期間 | 2020-05-15 - 2020-05-15 |
| 開催地（和） | オンライン開催 |
| 開催地（英） | Online |
| テーマ（和） | 一般 |
| テーマ（英） | |
| 講演論文情報の詳細 | |
| 申込み研究会 | NLP |
| 会議コード | 2020-05-NLP |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Spy Algorithm for Solving Continuous Optimization Problem |
| サブタイトル（英） | |
| キーワード(1)（和/英） | optimization / optimization |
| キーワード(2)（和/英） | continuous / continuous |
| キーワード(3)（和/英） | metaheuristic / metaheuristic |
| キーワード(4)（和/英） | / |
| キーワード(5)（和/英） | / |
| キーワード(6)（和/英） | / |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | Dhidhi Pambudi / Dhidhi Pambudi / |
| 第1著者 所属（和/英） | Yamaguchi University (略称： 山口大) Yamaguchi University (略称： Yamaguchi Univ.) |
| 第2著者 氏名（和/英/ヨミ） | Masaki Kawamura / Masaki Kawamura / |
| 第2著者 所属（和/英） | Yamaguchi University (略称： 山口大) Yamaguchi University (略称： Yamaguchi Univ.) |
| 講演者 | 第1著者 |
| 発表日時 | 2020-05-15 13:50:00 |
| 発表時間 | 25分 |
| 申込先研究会 | NLP |
| 資料番号 | NLP2020-5 |
| 巻番号（vol） | vol.120 |
| 号番号（no） | no.26 |
| ページ範囲 | pp.23-28 |
| ページ数 | 6 |
| 発行日 | 2020-05-08 (NLP) |
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