Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global searc...

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Vydáno v:	International journal of intelligent systems Ročník 37; číslo 6; s. 3193 - 3254
Hlavní autoři:	Qiao, Zenglin, Shan, Weifeng, Jiang, Nan, Heidari, Ali Asghar, Chen, Huiling, Teng, Yuntian, Turabieh, Hamza, Mafarja, Majdi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York John Wiley & Sons, Inc 01.06.2022
Témata:	Algorithms Artificial neural networks Back propagation networks Bones global optimization gradient‐based optimizer Intelligent systems kernel extreme learning machine Machine learning medical diagnosis Neural networks Newton methods Optimization parameter optimization Problem solving
ISSN:	0884-8173, 1098-111X
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Abstract	Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.
AbstractList	Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.
Author	Qiao, Zenglin Heidari, Ali Asghar Turabieh, Hamza Teng, Yuntian Mafarja, Majdi Jiang, Nan Shan, Weifeng Chen, Huiling
Author_xml	– sequence: 1 givenname: Zenglin orcidid: 0000-0002-3793-7150 surname: Qiao fullname: Qiao, Zenglin organization: School of Emergency Management, Institute of Disaster Prevention – sequence: 2 givenname: Weifeng orcidid: 0000-0002-9658-9483 surname: Shan fullname: Shan, Weifeng email: william.shan@gmail.com organization: Institute of Geophysics, China Earthquake Administration – sequence: 3 givenname: Nan orcidid: 0000-0003-1712-1872 surname: Jiang fullname: Jiang, Nan organization: College of Information Engineering, East China Jiaotong University – sequence: 4 givenname: Ali Asghar orcidid: 0000-0001-6938-9948 surname: Heidari fullname: Heidari, Ali Asghar organization: Wenzhou University – sequence: 5 givenname: Huiling orcidid: 0000-0002-7714-9693 surname: Chen fullname: Chen, Huiling email: chenhuiling.jlu@gmail.com organization: Wenzhou University – sequence: 6 givenname: Yuntian orcidid: 0000-0001-7810-4991 surname: Teng fullname: Teng, Yuntian email: tengyt@cea-igp.ac.cn organization: Institute of Geophysics, China Earthquake Administration – sequence: 7 givenname: Hamza orcidid: 0000-0002-8103-563X surname: Turabieh fullname: Turabieh, Hamza organization: College of Computers and Information Technology, Taif University – sequence: 8 givenname: Majdi orcidid: 0000-0002-0387-8252 surname: Mafarja fullname: Mafarja, Majdi organization: Birzeit University
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Publisher	John Wiley & Sons, Inc
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Snippet	Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this...
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SubjectTerms	Algorithms Artificial neural networks Back propagation networks Bones global optimization gradient‐based optimizer Intelligent systems kernel extreme learning machine Machine learning medical diagnosis Neural networks Newton methods Optimization parameter optimization Problem solving
Title	Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns
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