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An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem

This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local s...

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Main Authors: Zhang, Cheng Xu, Zhou, Kai Qing, Ye, Shao Qiang, Mohd. Zain, Azlan
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/95752/1/AzlanMohdZain2021_AnImprovedCuckooSearchAlgorithm.pdf
http://eprints.utm.my/id/eprint/95752/
http://dx.doi.org/10.1109/ACCESS.2021.3130640
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spelling my.utm.957522022-05-31T13:18:41Z http://eprints.utm.my/id/eprint/95752/ An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem Zhang, Cheng Xu Zhou, Kai Qing Ye, Shao Qiang Mohd. Zain, Azlan QA75 Electronic computers. Computer science This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local search capabilities. Compared with other CS variants, two strategies are proposed in this paper to improve the properties of the WCSDE. On the one hand, a non-linearly decreasing inertia weight with the number of evolutionary iterations is employed in the WCSDE to improve the update method of the bird's nest position, enhance the balance between the exploration and development capabilities, and strengthen the local optimization capability. On the other hand, the mutation and cross-selection mechanisms of the differential evolution (DE) algorithm are introduced to make up for the lack of the mutual relationship between the populations, avoid the loss of practical information, and increase the convergence accuracy. In the experiment part, 13 classic benchmark functions are selected to execute the function optimization tasks among the standard CS, the WCSDE, and other four CS variants to verify the effectiveness of the proposed algorithm from two aspects. The results and corresponding statistical analysis reveal that the proposed algorithm has better global search ability and strengthener robustness. Institute of Electrical and Electronics Engineers Inc. 2021-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95752/1/AzlanMohdZain2021_AnImprovedCuckooSearchAlgorithm.pdf Zhang, Cheng Xu and Zhou, Kai Qing and Ye, Shao Qiang and Mohd. Zain, Azlan (2021) An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem. IEEE Access, 9 . pp. 161352-161373. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2021.3130640 DOI:10.1109/ACCESS.2021.3130640
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zhang, Cheng Xu
Zhou, Kai Qing
Ye, Shao Qiang
Mohd. Zain, Azlan
An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem
description This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local search capabilities. Compared with other CS variants, two strategies are proposed in this paper to improve the properties of the WCSDE. On the one hand, a non-linearly decreasing inertia weight with the number of evolutionary iterations is employed in the WCSDE to improve the update method of the bird's nest position, enhance the balance between the exploration and development capabilities, and strengthen the local optimization capability. On the other hand, the mutation and cross-selection mechanisms of the differential evolution (DE) algorithm are introduced to make up for the lack of the mutual relationship between the populations, avoid the loss of practical information, and increase the convergence accuracy. In the experiment part, 13 classic benchmark functions are selected to execute the function optimization tasks among the standard CS, the WCSDE, and other four CS variants to verify the effectiveness of the proposed algorithm from two aspects. The results and corresponding statistical analysis reveal that the proposed algorithm has better global search ability and strengthener robustness.
format Article
author Zhang, Cheng Xu
Zhou, Kai Qing
Ye, Shao Qiang
Mohd. Zain, Azlan
author_facet Zhang, Cheng Xu
Zhou, Kai Qing
Ye, Shao Qiang
Mohd. Zain, Azlan
author_sort Zhang, Cheng Xu
title An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem
title_short An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem
title_full An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem
title_fullStr An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem
title_full_unstemmed An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem
title_sort improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url http://eprints.utm.my/id/eprint/95752/1/AzlanMohdZain2021_AnImprovedCuckooSearchAlgorithm.pdf
http://eprints.utm.my/id/eprint/95752/
http://dx.doi.org/10.1109/ACCESS.2021.3130640
_version_ 1735386842930348032
score 13.244369

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