Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies

Li HUANG; Xiao ZHENG; Shuai DING; Zhi LIU; Jun HUANG

doi:10.1587/transinf.2019EDP7013

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Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies

Li HUANG, Xiao ZHENG, Shuai DING, Zhi LIU, Jun HUANG

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Summary :

The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability P_c. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.

Publication: IEICE TRANSACTIONS on Information Vol.E102-D No.10 pp.1916-1924

Publication Date: 2019/10/01

Publicized: 2019/07/09

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDP7013

Type of Manuscript: PAPER

Category: Fundamentals of Information Systems

Authors

Li HUANG
  Hefei University of Technology,Anhui University of Technology
Xiao ZHENG
  Anhui University of Technology
Shuai DING
  Hefei University of Technology
Zhi LIU
  Shizuoka University
Jun HUANG
  Anhui University of Technology

Keyword

multi-learning strategy, cuckoo search, bound handling mechanism, elite learning

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Li HUANG, Xiao ZHENG, Shuai DING, Zhi LIU, Jun HUANG, "Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies" in IEICE TRANSACTIONS on Information, vol. E102-D, no. 10, pp. 1916-1924, October 2019, doi: 10.1587/transinf.2019EDP7013.
Abstract: The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability P_c. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7013/_p

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@ARTICLE{e102-d_10_1916,
author={Li HUANG, Xiao ZHENG, Shuai DING, Zhi LIU, Jun HUANG, },
journal={IEICE TRANSACTIONS on Information},
title={Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies},
year={2019},
volume={E102-D},
number={10},
pages={1916-1924},
abstract={The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability P_c. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.},
keywords={},
doi={10.1587/transinf.2019EDP7013},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies
T2 - IEICE TRANSACTIONS on Information
SP - 1916
EP - 1924
AU - Li HUANG
AU - Xiao ZHENG
AU - Shuai DING
AU - Zhi LIU
AU - Jun HUANG
PY - 2019
DO - 10.1587/transinf.2019EDP7013
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2019
AB - The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability P_c. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.
ER -