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Gaussian bare-bones cuckoo search algorithm

Authors:

Hu Peng,

Zhijian WuAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 93 - 94

https://doi.org/10.1145/3205651.3205666

Published: 06 July 2018 Publication History

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Abstract

Cuckoo search (CS), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimization. However, the premature convergence has greatly affected the performance of original CS. Inspired from the individuals in the population will converge to the weighted average of global best and personal best in particle swarm optimization (PSO), we proposed a novel Gaussian bare-bones CS algorithm, named GBCS, in which the new solution for a cuckoo is generated by the Lévy flight or the Gaussian bare-bones method in a random manner. Experimental results have proved that the proposed algorithm is promising.

References

[1]

Maurice Clerc and James Kennedy. 2002. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation 6, 1 (2002), 58--73.

Digital Library

Google Scholar

[2]

J Kennedy and R Eberhart. 1995. Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, Vol. 4. IEEE, 1942--1948.

Crossref

Google Scholar

[3]

Xiangtao Li and Minghao Yin. 2015. Modified cuckoo search algorithm with self adaptive parameter method. Information Sciences 298 (2015), 80--97.

Digital Library

Google Scholar

[4]

Mohammad Shehab, Ahamad Tajudin Khader, and Mohammed Azmi Al-Betar. 2017. A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing (2017).

Google Scholar

[5]

Hui Wang, Wenjun Wang, Hui Sun, Zhihua Cui, Shahryar Rahnamayan, and Sanyou Zeng. 2017. A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Computing 21, 15 (2017), 1--11.

Digital Library

Google Scholar

[6]

Xin-She Yang and Suash Deb. 2009. Cuckoo search via Lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, 210--214.

Google Scholar

[7]

Xin Yao, Yong Liu, and Guangming Lin. 1999. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 2 (1999), 82--102.

Digital Library

Google Scholar

Cited By

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Luo ZXiong JPeng HZhan GZhang QWang HZhou XLi WHuang YLi XHandl J(2024)A Micro Dynamic Multi-objective Evolutionary Algorithm for Small-scale Smart Greenhouse with Low-power MicroprocessorProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654276(687-690)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654276
Xu WYu X(2022)Adaptive Guided Spatial Compressive Cuckoo Search for Optimization ProblemsMathematics10.3390/math1003049510:3(495)Online publication date: 3-Feb-2022
https://doi.org/10.3390/math10030495
Qu ZDong YMugemanyi SYu TBo XLi HLi YRugema FBananeza C(2022)Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power systemIET Renewable Power Generation10.1049/rpg2.1242816:7(1401-1424)Online publication date: 24-Feb-2022
https://doi.org/10.1049/rpg2.12428
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Index Terms

Gaussian bare-bones cuckoo search algorithm
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
      2. Neural networks

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Published In

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2018

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The National Natural Science Foundation of China

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GECCO '18

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SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Luo ZXiong JPeng HZhan GZhang QWang HZhou XLi WHuang YLi XHandl J(2024)A Micro Dynamic Multi-objective Evolutionary Algorithm for Small-scale Smart Greenhouse with Low-power MicroprocessorProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654276(687-690)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654276
Xu WYu X(2022)Adaptive Guided Spatial Compressive Cuckoo Search for Optimization ProblemsMathematics10.3390/math1003049510:3(495)Online publication date: 3-Feb-2022
https://doi.org/10.3390/math10030495
Qu ZDong YMugemanyi SYu TBo XLi HLi YRugema FBananeza C(2022)Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power systemIET Renewable Power Generation10.1049/rpg2.1242816:7(1401-1424)Online publication date: 24-Feb-2022
https://doi.org/10.1049/rpg2.12428
Luo WYu X(2022)Quasi-reflection based multi-strategy cuckoo search for parameter estimation of photovoltaic solar modulesSolar Energy10.1016/j.solener.2022.08.004243(264-278)Online publication date: Sep-2022
https://doi.org/10.1016/j.solener.2022.08.004
Luo WYu X(2022)Reinforcement learning-based modified cuckoo search algorithm for economic dispatch problemsKnowledge-Based Systems10.1016/j.knosys.2022.109844257(109844)Online publication date: Dec-2022
https://doi.org/10.1016/j.knosys.2022.109844
Peng HZeng ZDeng CWu Z(2020)Multi-strategy serial cuckoo search algorithm for global optimizationKnowledge-Based Systems10.1016/j.knosys.2020.106729(106729)Online publication date: Dec-2020
https://doi.org/10.1016/j.knosys.2020.106729
El-Abd M(2019)Gaussian Bare-Bones Brain Storm Optimization Algorithm2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790208(227-233)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8790208
He ZPeng HDeng CTan YWu ZWu S(2019)A Spark-based Gaussian Bare-bones Cuckoo Search with dynamic parameter selection2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790040(1220-1227)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8790040

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A bare bones bacterial foraging optimization algorithm

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A hybridization of cuckoo search and particle swarm optimization for solving optimization problems

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