Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm

Parimal Kumar Giri; Sagar S. De; Satchidananda Dehuri; Sung Bae Cho

doi:10.1109/ICIT.2017.44

Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm

Parimal Kumar Giri, Sagar S. De, Satchidananda Dehuri, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

In this paper, we have used chaotic maps to improve locally and globally tuned biogeography-based optimization (LGBBO) algorithm. The effect of chaotic maps like Chebyshev, Logistic, Sinusoidal, and Circle for enhancing the efficiency of LGBBO are studied in terms of local optima avoidance and convergence speed, and we name it as Locally and Globally Tuned Chaotic BBO(LGCBBO). We have carried out an extensive numerical evaluation on ten high dimensional benchmark functions to measure the efficiency of our proposed method. The experimental study confirms that LGCBBO is better than LGBBO for some chaotic maps in terms of accuracy and convergence rate.

Original language	English
Title of host publication	Proceedings - 2017 International Conference on Information Technology, ICIT 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	152-158
Number of pages	7
ISBN (Print)	9781538629246
DOIs	https://doi.org/10.1109/ICIT.2017.44
Publication status	Published - 2018 Jul 31
Event	16th International Conference on Information Technology, ICIT 2017 - Bhubaneswar, Odisha, India Duration: 2017 Dec 21 → 2017 Dec 23

Publication series

Name	Proceedings - 2017 International Conference on Information Technology, ICIT 2017

Other

Other	16th International Conference on Information Technology, ICIT 2017
Country/Territory	India
City	Bhubaneswar, Odisha
Period	17/12/21 → 17/12/23

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Safety, Risk, Reliability and Quality
Computer Networks and Communications
Computer Science Applications
Software
Information Systems and Management

Access to Document

10.1109/ICIT.2017.44

Cite this

Giri, P. K., De, S. S., Dehuri, S., & Cho, S. B. (2018). Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm. In Proceedings - 2017 International Conference on Information Technology, ICIT 2017 (pp. 152-158). Article 8423899 (Proceedings - 2017 International Conference on Information Technology, ICIT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2017.44

Giri, Parimal Kumar ; De, Sagar S. ; Dehuri, Satchidananda et al. / Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm. Proceedings - 2017 International Conference on Information Technology, ICIT 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 152-158 (Proceedings - 2017 International Conference on Information Technology, ICIT 2017).

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title = "Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm",

abstract = "In this paper, we have used chaotic maps to improve locally and globally tuned biogeography-based optimization (LGBBO) algorithm. The effect of chaotic maps like Chebyshev, Logistic, Sinusoidal, and Circle for enhancing the efficiency of LGBBO are studied in terms of local optima avoidance and convergence speed, and we name it as Locally and Globally Tuned Chaotic BBO(LGCBBO). We have carried out an extensive numerical evaluation on ten high dimensional benchmark functions to measure the efficiency of our proposed method. The experimental study confirms that LGCBBO is better than LGBBO for some chaotic maps in terms of accuracy and convergence rate.",

author = "Giri, {Parimal Kumar} and De, {Sagar S.} and Satchidananda Dehuri and Cho, {Sung Bae}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th International Conference on Information Technology, ICIT 2017 ; Conference date: 21-12-2017 Through 23-12-2017",

year = "2018",

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Giri, PK, De, SS, Dehuri, S & Cho, SB 2018, Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm. in Proceedings - 2017 International Conference on Information Technology, ICIT 2017., 8423899, Proceedings - 2017 International Conference on Information Technology, ICIT 2017, Institute of Electrical and Electronics Engineers Inc., pp. 152-158, 16th International Conference on Information Technology, ICIT 2017, Bhubaneswar, Odisha, India, 17/12/21. https://doi.org/10.1109/ICIT.2017.44

Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm. / Giri, Parimal Kumar; De, Sagar S.; Dehuri, Satchidananda et al.
Proceedings - 2017 International Conference on Information Technology, ICIT 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 152-158 8423899 (Proceedings - 2017 International Conference on Information Technology, ICIT 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm

AU - Giri, Parimal Kumar

AU - De, Sagar S.

AU - Dehuri, Satchidananda

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N2 - In this paper, we have used chaotic maps to improve locally and globally tuned biogeography-based optimization (LGBBO) algorithm. The effect of chaotic maps like Chebyshev, Logistic, Sinusoidal, and Circle for enhancing the efficiency of LGBBO are studied in terms of local optima avoidance and convergence speed, and we name it as Locally and Globally Tuned Chaotic BBO(LGCBBO). We have carried out an extensive numerical evaluation on ten high dimensional benchmark functions to measure the efficiency of our proposed method. The experimental study confirms that LGCBBO is better than LGBBO for some chaotic maps in terms of accuracy and convergence rate.

AB - In this paper, we have used chaotic maps to improve locally and globally tuned biogeography-based optimization (LGBBO) algorithm. The effect of chaotic maps like Chebyshev, Logistic, Sinusoidal, and Circle for enhancing the efficiency of LGBBO are studied in terms of local optima avoidance and convergence speed, and we name it as Locally and Globally Tuned Chaotic BBO(LGCBBO). We have carried out an extensive numerical evaluation on ten high dimensional benchmark functions to measure the efficiency of our proposed method. The experimental study confirms that LGCBBO is better than LGBBO for some chaotic maps in terms of accuracy and convergence rate.

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Giri PK, De SS, Dehuri S, Cho SB. Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm. In Proceedings - 2017 International Conference on Information Technology, ICIT 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 152-158. 8423899. (Proceedings - 2017 International Conference on Information Technology, ICIT 2017). doi: 10.1109/ICIT.2017.44

Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm

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