Biological survival optimization algorithm with its engineering and neural network applications

Likai Wang; Qingyang Zhang; Xiangyu He; Shengxiang Yang; Shouyong Jiang; Yongquan Dong

doi:10.1007/s00500-023-07851-4

Biological survival optimization algorithm with its engineering and neural network applications

Likai Wang, Qingyang Zhang^* (Corresponding Author), Xiangyu He, Shengxiang Yang, Shouyong Jiang^* (Corresponding Author), Yongquan Dong

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.

Original language	English
Pages (from-to)	6437–6463
Number of pages	7
Journal	Soft Computing
Volume	27
Early online date	13 Feb 2023
DOIs	https://doi.org/10.1007/s00500-023-07851-4
Publication status	Published - 1 May 2023

Bibliographical note

Funding Information:
The authors express sincerely appreciation to the anonymous reviewers for their helpful opinions. This work is supported by the National Natural Science Foundation of China under Grants 62006103 and 61872168, in part by the National Science Foundation of Jiangsu higher education institutions under Grand 20KJB110021, in part by the Postgraduate research and practice innovation program of Jiangsu Province under Grand KYCX222858, and in part by the Royal Society International Exchanges Scheme IEC NSFC 211404.

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Data Availability Statement

The data sets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Keywords

Biological survival optimizer
Engineering structural problem
Escape behavior
Neural network

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10.1007/s00500-023-07851-4Licence: Unspecified

Cite this

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title = "Biological survival optimization algorithm with its engineering and neural network applications",

abstract = "This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.",

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AU - Dong, Yongquan

N1 - Funding Information: The authors express sincerely appreciation to the anonymous reviewers for their helpful opinions. This work is supported by the National Natural Science Foundation of China under Grants 62006103 and 61872168, in part by the National Science Foundation of Jiangsu higher education institutions under Grand 20KJB110021, in part by the Postgraduate research and practice innovation program of Jiangsu Province under Grand KYCX222858, and in part by the Royal Society International Exchanges Scheme IEC NSFC 211404. Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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N2 - This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.

AB - This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.

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Biological survival optimization algorithm with its engineering and neural network applications

Abstract

Bibliographical note

Data Availability Statement

Keywords

Access to Document

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