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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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 66084 - 66109
Main Authors: Wei, Zhenglei, Huang, Changqiang, Wang, Xiaofei, Han, Tong, Li, Yintong
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2918406