Biologically adaptive artificial bee colony for numerical optimization

Sheng Ta Hsieh; Hao Wen Cheng; Chun Ling Lin; Tsung Ying Sun

doi:10.1109/CANDAR.2017.94

Biologically adaptive artificial bee colony for numerical optimization

Sheng Ta Hsieh
, Hao Wen Cheng
, Chun Ling Lin
, Tsung Ying Sun

電子工程系

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

2 引文斯高帕斯（Scopus）

摘要

The artificial bee colony (ABC) algorithm is a well-known algorithm for solving numerical optimal problems. In this paper, a biologically adaptive artificial bee colony (BAABC) algorithm is proposed. In BAABC, the initialization of population is performed by the Halton Sequence so the population is generated in potential solution space. Besides, the size of population is varying according to status of solution searching. If BAABC finds a good solution, the size of population is increased for enhancing solution searching and vice versa. To examine the proposed method, fifteen evaluated functions are adopted in experiments. The result shows that BAABC performs better than related ABC methods on most of the test function.

原文	English
主出版物標題	Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	187-193
頁數	7
ISBN（電子）	9781538620878
DOIs	https://doi.org/10.1109/CANDAR.2017.94
出版狀態	Published - 2 7月 2017
事件	5th International Symposium on Computing and Networking, CANDAR 2017 - Aomori, Japan 持續時間: 19 11月 2017 → 22 11月 2017

出版系列

名字	Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017
卷	2018-January

Conference

Conference	5th International Symposium on Computing and Networking, CANDAR 2017
國家/地區	Japan
城市	Aomori
期間	19/11/17 → 22/11/17

文獻附註

Publisher Copyright:
© 2017 IEEE.

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10.1109/CANDAR.2017.94

其它文件與鏈接

Link to publication in Scopus

引用此

Hsieh, S. T., Cheng, H. W., Lin, C. L., & Sun, T. Y. (2017). Biologically adaptive artificial bee colony for numerical optimization. 於 Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017 (頁 187-193). (Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017; 卷 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CANDAR.2017.94

Hsieh, Sheng Ta ; Cheng, Hao Wen ; Lin, Chun Ling 等. / Biologically adaptive artificial bee colony for numerical optimization. Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 頁 187-193 (Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017).

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title = "Biologically adaptive artificial bee colony for numerical optimization",

abstract = "The artificial bee colony (ABC) algorithm is a well-known algorithm for solving numerical optimal problems. In this paper, a biologically adaptive artificial bee colony (BAABC) algorithm is proposed. In BAABC, the initialization of population is performed by the Halton Sequence so the population is generated in potential solution space. Besides, the size of population is varying according to status of solution searching. If BAABC finds a good solution, the size of population is increased for enhancing solution searching and vice versa. To examine the proposed method, fifteen evaluated functions are adopted in experiments. The result shows that BAABC performs better than related ABC methods on most of the test function.",

keywords = "adaptive, artificial bee colony, Halton sequence, numerical optimization",

author = "Hsieh, \{Sheng Ta\} and Cheng, \{Hao Wen\} and Lin, \{Chun Ling\} and Sun, \{Tsung Ying\}",

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Hsieh, ST, Cheng, HW, Lin, CL & Sun, TY 2017, Biologically adaptive artificial bee colony for numerical optimization. 於 Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017. Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017, 卷 2018-January, Institute of Electrical and Electronics Engineers Inc., 頁 187-193, 5th International Symposium on Computing and Networking, CANDAR 2017, Aomori, Japan, 19/11/17. https://doi.org/10.1109/CANDAR.2017.94

Biologically adaptive artificial bee colony for numerical optimization. / Hsieh, Sheng Ta; Cheng, Hao Wen; Lin, Chun Ling 等.
Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 187-193 (Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017; 卷 2018-January).

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

TY - GEN

T1 - Biologically adaptive artificial bee colony for numerical optimization

AU - Hsieh, Sheng Ta

AU - Cheng, Hao Wen

AU - Lin, Chun Ling

AU - Sun, Tsung Ying

PY - 2017/7/2

Y1 - 2017/7/2

N2 - The artificial bee colony (ABC) algorithm is a well-known algorithm for solving numerical optimal problems. In this paper, a biologically adaptive artificial bee colony (BAABC) algorithm is proposed. In BAABC, the initialization of population is performed by the Halton Sequence so the population is generated in potential solution space. Besides, the size of population is varying according to status of solution searching. If BAABC finds a good solution, the size of population is increased for enhancing solution searching and vice versa. To examine the proposed method, fifteen evaluated functions are adopted in experiments. The result shows that BAABC performs better than related ABC methods on most of the test function.

AB - The artificial bee colony (ABC) algorithm is a well-known algorithm for solving numerical optimal problems. In this paper, a biologically adaptive artificial bee colony (BAABC) algorithm is proposed. In BAABC, the initialization of population is performed by the Halton Sequence so the population is generated in potential solution space. Besides, the size of population is varying according to status of solution searching. If BAABC finds a good solution, the size of population is increased for enhancing solution searching and vice versa. To examine the proposed method, fifteen evaluated functions are adopted in experiments. The result shows that BAABC performs better than related ABC methods on most of the test function.

KW - adaptive

KW - artificial bee colony

KW - Halton sequence

KW - numerical optimization

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U2 - 10.1109/CANDAR.2017.94

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T3 - Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017

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BT - Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Symposium on Computing and Networking, CANDAR 2017

Y2 - 19 November 2017 through 22 November 2017

ER -

Hsieh ST, Cheng HW, Lin CL, Sun TY. Biologically adaptive artificial bee colony for numerical optimization. 於 Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 187-193. (Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017). doi: 10.1109/CANDAR.2017.94

Biologically adaptive artificial bee colony for numerical optimization

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