An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations

Zhiren Zan; Yuanzhi Cong; Xinming Zhang

doi:10.1145/3545801.3545812

An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations

Zhiren Zan
, Yuanzhi Cong
, Xinming Zhang

Harbin Institute of Technology Shenzhen

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

Abstract

This paper introduces a new hybrid bat algorithm for solving nonlinear equations. Nonlinear equations are often solved by using various optimization algorithms in practice. Bat algorithm is a new intelligent optimization algorithm proposed by Yang in 2010. However, in its practical application, there are some disadvantages, for example, it is easy to fall into local optimization. Therefore, this paper introduces four methods: adaptive inertia weight, chaotic local search, differential evolution algorithm and cross entropy method to optimize the bat algorithm. In this paper, the implementation of four algorithms is first introduced, and then four examples of nonlinear system of equations are to be calculated by using the hybrid self-adaptive bat algorithm (HSABA) to prove the practical feasibility of these four methods.

Original language	English
Title of host publication	ICBDC 2022 - 2022 7th International Conference on Big Data and Computing
Publisher	Association for Computing Machinery
Pages	75-81
Number of pages	7
ISBN (Electronic)	9781450396097
DOIs	https://doi.org/10.1145/3545801.3545812
State	Published - 27 May 2022
Externally published	Yes
Event	7th International Conference on Big Data and Computing, ICBDC 2022 - Virtual, Online, China Duration: 27 May 2022 → 29 May 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	7th International Conference on Big Data and Computing, ICBDC 2022
Country/Territory	China
City	Virtual, Online
Period	27/05/22 → 29/05/22

Keywords

Adaptive inertia weigh
Bat algorithm
Chaotic local search
Cross entropy method
Differential evolution algorithm

Access to Document

10.1145/3545801.3545812

Cite this

@inproceedings{5d573100ac6544dd877a8026e71a797b,

title = "An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations",

abstract = "This paper introduces a new hybrid bat algorithm for solving nonlinear equations. Nonlinear equations are often solved by using various optimization algorithms in practice. Bat algorithm is a new intelligent optimization algorithm proposed by Yang in 2010. However, in its practical application, there are some disadvantages, for example, it is easy to fall into local optimization. Therefore, this paper introduces four methods: adaptive inertia weight, chaotic local search, differential evolution algorithm and cross entropy method to optimize the bat algorithm. In this paper, the implementation of four algorithms is first introduced, and then four examples of nonlinear system of equations are to be calculated by using the hybrid self-adaptive bat algorithm (HSABA) to prove the practical feasibility of these four methods.",

keywords = "Adaptive inertia weigh, Bat algorithm, Chaotic local search, Cross entropy method, Differential evolution algorithm",

author = "Zhiren Zan and Yuanzhi Cong and Xinming Zhang",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 7th International Conference on Big Data and Computing, ICBDC 2022 ; Conference date: 27-05-2022 Through 29-05-2022",

year = "2022",

month = may,

day = "27",

doi = "10.1145/3545801.3545812",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

pages = "75--81",

booktitle = "ICBDC 2022 - 2022 7th International Conference on Big Data and Computing",

address = "美国",

}

Zan, Z, Cong, Y & Zhang, X 2022, An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations. in ICBDC 2022 - 2022 7th International Conference on Big Data and Computing. ACM International Conference Proceeding Series, Association for Computing Machinery , pp. 75-81, 7th International Conference on Big Data and Computing, ICBDC 2022, Virtual, Online, China, 27/05/22. https://doi.org/10.1145/3545801.3545812

An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations. / Zan, Zhiren; Cong, Yuanzhi; Zhang, Xinming.
ICBDC 2022 - 2022 7th International Conference on Big Data and Computing. Association for Computing Machinery , 2022. p. 75-81 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations

AU - Zan, Zhiren

AU - Cong, Yuanzhi

AU - Zhang, Xinming

PY - 2022/5/27

Y1 - 2022/5/27

N2 - This paper introduces a new hybrid bat algorithm for solving nonlinear equations. Nonlinear equations are often solved by using various optimization algorithms in practice. Bat algorithm is a new intelligent optimization algorithm proposed by Yang in 2010. However, in its practical application, there are some disadvantages, for example, it is easy to fall into local optimization. Therefore, this paper introduces four methods: adaptive inertia weight, chaotic local search, differential evolution algorithm and cross entropy method to optimize the bat algorithm. In this paper, the implementation of four algorithms is first introduced, and then four examples of nonlinear system of equations are to be calculated by using the hybrid self-adaptive bat algorithm (HSABA) to prove the practical feasibility of these four methods.

AB - This paper introduces a new hybrid bat algorithm for solving nonlinear equations. Nonlinear equations are often solved by using various optimization algorithms in practice. Bat algorithm is a new intelligent optimization algorithm proposed by Yang in 2010. However, in its practical application, there are some disadvantages, for example, it is easy to fall into local optimization. Therefore, this paper introduces four methods: adaptive inertia weight, chaotic local search, differential evolution algorithm and cross entropy method to optimize the bat algorithm. In this paper, the implementation of four algorithms is first introduced, and then four examples of nonlinear system of equations are to be calculated by using the hybrid self-adaptive bat algorithm (HSABA) to prove the practical feasibility of these four methods.

KW - Adaptive inertia weigh

KW - Bat algorithm

KW - Chaotic local search

KW - Cross entropy method

KW - Differential evolution algorithm

UR - https://www.scopus.com/pages/publications/85138331478

U2 - 10.1145/3545801.3545812

DO - 10.1145/3545801.3545812

M3 - 会议稿件

AN - SCOPUS:85138331478

T3 - ACM International Conference Proceeding Series

SP - 75

EP - 81

BT - ICBDC 2022 - 2022 7th International Conference on Big Data and Computing

PB - Association for Computing Machinery

T2 - 7th International Conference on Big Data and Computing, ICBDC 2022

Y2 - 27 May 2022 through 29 May 2022

ER -

An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations

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Keywords

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