Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NRO) is presented. Meanwhile, NRO imitates the nuclear reaction...

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Veröffentlicht in:	IEEE access Jg. 7; S. 66084 - 66109
Hauptverfasser:	Wei, Zhenglei, Huang, Changqiang, Wang, Xiaofei, Han, Tong, Li, Yintong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Benchmark testing Benchmarks Chemicals Classification algorithms Design engineering Design optimization Differential evolution operator engineering design optimization Evolutionary computation Exploitation Exploration Global optimization Heuristic algorithms Heuristic methods Levy flight strategy Nuclear fission Nuclear fusion nuclear reaction optimization (NRO) Nuclear reactions Operators (mathematics) Optimization physics-based algorithm Sociology Statistics
ISSN:	2169-3536, 2169-3536
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Abstract	Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NRO) is presented. Meanwhile, NRO imitates the nuclear reaction process and consists of two phases, namely, a nuclear fission (NFi) phase and a nuclear fusion (NFu) phase. The Gaussian walk and differential evolution operators between nucleus and neutron are employed for exploitation and appropriate exploration in the (NFi) phase, respectively. Meanwhile, the variants of differential evolution operator are utilized for exploration in the NFu phase, which consists of the ionization and fusion stages. Additionally, the variants of Levy flight are used for random searching to escape from the local optima in each stage of the NFu phase. The exploration and exploitation abilities of NRO can be balanced due to a combination of the two phases. Both constrained and unconstrained benchmark functions are employed for testing the performance of NRO. To make comparisons between NRO and the state-of-the-art algorithms, 23 classic benchmark functions and twenty-night modern benchmark functions are performed. Moreover, three engineering design optimization problems are solved as constrained benchmark functions by using NRO and the compared algorithms. The results illustrate that the proposed nuclear reaction optimization algorithm is a potential and powerful approach for global optimization.
AbstractList	Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NRO) is presented. Meanwhile, NRO imitates the nuclear reaction process and consists of two phases, namely, a nuclear fission (NFi) phase and a nuclear fusion (NFu) phase. The Gaussian walk and differential evolution operators between nucleus and neutron are employed for exploitation and appropriate exploration in the (NFi) phase, respectively. Meanwhile, the variants of differential evolution operator are utilized for exploration in the NFu phase, which consists of the ionization and fusion stages. Additionally, the variants of Levy flight are used for random searching to escape from the local optima in each stage of the NFu phase. The exploration and exploitation abilities of NRO can be balanced due to a combination of the two phases. Both constrained and unconstrained benchmark functions are employed for testing the performance of NRO. To make comparisons between NRO and the state-of-the-art algorithms, 23 classic benchmark functions and twenty-night modern benchmark functions are performed. Moreover, three engineering design optimization problems are solved as constrained benchmark functions by using NRO and the compared algorithms. The results illustrate that the proposed nuclear reaction optimization algorithm is a potential and powerful approach for global optimization.
Author	Li, Yintong Wang, Xiaofei Han, Tong Wei, Zhenglei Huang, Changqiang
Author_xml	– sequence: 1 givenname: Zhenglei orcidid: 0000-0002-2530-8365 surname: Wei fullname: Wei, Zhenglei email: zhenglei_wei@126.com organization: Institute of Aeronautics Engineering, Air Force Engineering University, Xi'an, China – sequence: 2 givenname: Changqiang surname: Huang fullname: Huang, Changqiang organization: Institute of Aeronautics Engineering, Air Force Engineering University, Xi'an, China – sequence: 3 givenname: Xiaofei orcidid: 0000-0002-8482-6138 surname: Wang fullname: Wang, Xiaofei organization: Institute of Aeronautics Engineering, Air Force Engineering University, Xi'an, China – sequence: 4 givenname: Tong surname: Han fullname: Han, Tong organization: Institute of Aeronautics Engineering, Air Force Engineering University, Xi'an, China – sequence: 5 givenname: Yintong surname: Li fullname: Li, Yintong organization: Institute of Aeronautics Engineering, Air Force Engineering University, Xi'an, China
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SubjectTerms	Algorithms Benchmark testing Benchmarks Chemicals Classification algorithms Design engineering Design optimization Differential evolution operator engineering design optimization Evolutionary computation Exploitation Exploration Global optimization Heuristic algorithms Heuristic methods Levy flight strategy Nuclear fission Nuclear fusion nuclear reaction optimization (NRO) Nuclear reactions Operators (mathematics) Optimization physics-based algorithm Sociology Statistics
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Title	Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization
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