Adaptive Multi-scale Quantum Harmonic Oscillator Algorithm Based on Evolutionary Strategy
This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search...
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| Vydáno v: | 2020 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 8 |
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IEEE
01.07.2020
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| Abstract | This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search scale, the searching step decreases too fast at the later stage of the evolution and is more likely to suffer premature convergence and stagnation. To improve the convergence performance, an adaptive attenuation mechanism of scaling is proposed to dynamically adjust the exploration and exploitation properties. Evolutionary strategies such as selection, crossover and DE/rand/1 mutation are implemented in the proposed algorithm to enhance the exploration and exploitation abilities. Experimental results evaluated on several unimodal and multimodal benchmark functions indicate the significant improvement of the proposed algorithm to the original MQHOA. Meanwhile, the experimental results compared with several state-of-the-art optimizers show the superiority or competitiveness of the proposed algorithm. |
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| AbstractList | This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search scale, the searching step decreases too fast at the later stage of the evolution and is more likely to suffer premature convergence and stagnation. To improve the convergence performance, an adaptive attenuation mechanism of scaling is proposed to dynamically adjust the exploration and exploitation properties. Evolutionary strategies such as selection, crossover and DE/rand/1 mutation are implemented in the proposed algorithm to enhance the exploration and exploitation abilities. Experimental results evaluated on several unimodal and multimodal benchmark functions indicate the significant improvement of the proposed algorithm to the original MQHOA. Meanwhile, the experimental results compared with several state-of-the-art optimizers show the superiority or competitiveness of the proposed algorithm. |
| Author | Wang, Peng Ye, Xinggui |
| Author_xml | – sequence: 1 givenname: Xinggui surname: Ye fullname: Ye, Xinggui organization: University of Chinese Academy of Sciences,Beijing,China – sequence: 2 givenname: Peng surname: Wang fullname: Wang, Peng organization: Southwest Minzu University,School of Computer Science and Technology,Chengdu,China |
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| Snippet | This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical... |
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| SubjectTerms | adaptive mechanism Benchmark testing Convergence differential evolution Evolutionary strategy Harmonic analysis multi-scale quantum harmonic oscillator algorithm Oscillators population-based optimization Sociology Statistics |
| Title | Adaptive Multi-scale Quantum Harmonic Oscillator Algorithm Based on Evolutionary Strategy |
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