Alternative Mutation Operators in Collaborative Neurodynamic Optimization

Xinqi LI; Jun WANG; Sam KWONG

doi:10.1109/ICIST49303.2020.9202136

Alternative Mutation Operators in Collaborative Neurodynamic Optimization

Xinqi LI, Jun WANG, Sam KWONG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

3 Citations (Scopus)

Abstract

Collaborative neurodynamic optimization (CNO) is a global search technique that integrates the neurodynamic optimization with a swarm intelligence algorithm. Diversity is a key point for global optimization. Mutation operator is employed in CNO to ensure diversity. In the existing literature, the diversification performance of different mutation operators is unknown. In this paper, four mutation operators, Gaussian, Cauchy, Levy and wavelet mutations, are analyzed to compare the performances of mutation operators. Simulation results on four benchmark multimodal functions are discussed.

Original language	English
Title of host publication	10th International Conference on Information Science and Technology, ICIST 2020
Pages	126-133
DOIs	https://doi.org/10.1109/ICIST49303.2020.9202136
Publication status	Published - 11 Sept 2020
Externally published	Yes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under grant 61673330 and by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grants 11208517 and 11202318, and by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China (Grant No. 2018AAA0101301).

Keywords

collaborative neurodynamic optimization
global optimization
mutation operator

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10.1109/ICIST49303.2020.9202136

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title = "Alternative Mutation Operators in Collaborative Neurodynamic Optimization",

abstract = "Collaborative neurodynamic optimization (CNO) is a global search technique that integrates the neurodynamic optimization with a swarm intelligence algorithm. Diversity is a key point for global optimization. Mutation operator is employed in CNO to ensure diversity. In the existing literature, the diversification performance of different mutation operators is unknown. In this paper, four mutation operators, Gaussian, Cauchy, Levy and wavelet mutations, are analyzed to compare the performances of mutation operators. Simulation results on four benchmark multimodal functions are discussed.",

keywords = "collaborative neurodynamic optimization, global optimization, mutation operator",

author = "Xinqi LI and Jun WANG and Sam KWONG",

note = "This work was supported in part by the National Natural Science Foundation of China under grant 61673330 and by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grants 11208517 and 11202318, and by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China (Grant No. 2018AAA0101301).",

year = "2020",

month = sep,

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doi = "10.1109/ICIST49303.2020.9202136",

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AU - WANG, Jun

AU - KWONG, Sam

N1 - This work was supported in part by the National Natural Science Foundation of China under grant 61673330 and by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grants 11208517 and 11202318, and by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China (Grant No. 2018AAA0101301).

PY - 2020/9/11

Y1 - 2020/9/11

N2 - Collaborative neurodynamic optimization (CNO) is a global search technique that integrates the neurodynamic optimization with a swarm intelligence algorithm. Diversity is a key point for global optimization. Mutation operator is employed in CNO to ensure diversity. In the existing literature, the diversification performance of different mutation operators is unknown. In this paper, four mutation operators, Gaussian, Cauchy, Levy and wavelet mutations, are analyzed to compare the performances of mutation operators. Simulation results on four benchmark multimodal functions are discussed.

AB - Collaborative neurodynamic optimization (CNO) is a global search technique that integrates the neurodynamic optimization with a swarm intelligence algorithm. Diversity is a key point for global optimization. Mutation operator is employed in CNO to ensure diversity. In the existing literature, the diversification performance of different mutation operators is unknown. In this paper, four mutation operators, Gaussian, Cauchy, Levy and wavelet mutations, are analyzed to compare the performances of mutation operators. Simulation results on four benchmark multimodal functions are discussed.

KW - collaborative neurodynamic optimization

KW - global optimization

KW - mutation operator

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U2 - 10.1109/ICIST49303.2020.9202136

DO - 10.1109/ICIST49303.2020.9202136

M3 - Conference paper (refereed)

SP - 126

EP - 133

BT - 10th International Conference on Information Science and Technology, ICIST 2020

ER -

Alternative Mutation Operators in Collaborative Neurodynamic Optimization

Abstract

Bibliographical note

Keywords

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