Volume 30 Issue 1
Jan.  2021
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ZHAO Shijie, MA Shilin, GAO Leifu, YU Dongmei. A Novel Quantum Entanglement-Inspired Meta-heuristic Framework for Solving Multimodal Optimization Problems[J]. Chinese Journal of Electronics, 2021, 30(1): 145-152. doi: 10.1049/cje.2020.11.012
Citation: ZHAO Shijie, MA Shilin, GAO Leifu, YU Dongmei. A Novel Quantum Entanglement-Inspired Meta-heuristic Framework for Solving Multimodal Optimization Problems[J]. Chinese Journal of Electronics, 2021, 30(1): 145-152. doi: 10.1049/cje.2020.11.012

A Novel Quantum Entanglement-Inspired Meta-heuristic Framework for Solving Multimodal Optimization Problems

doi: 10.1049/cje.2020.11.012
Funds:

the Project of Liaoning Provincial Department of Education LJ2019JL017

the Doctoral Scientific Research Foundation of Liaoning Province 2019-BS-118

the Key Research Program of Liaoning Province LJ2019ZL001

National Natural Science Foundation of Liaoning 2019-ZD-0032

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  • Author Bio:

    MA Shilin  was born in 1997. He is a master student at Liaoning Technical University. His main research direction is intelligence optimization and computation, machine learning and data analytics

  • Corresponding author: ZHAO Shijie  (corresponding author)  was born in 1987. He received the Ph.D. degree from Liaoning Technical University. He is a lecturer at Liaoning Technical University. His research interests include intelligence optimization and computation, machine learning and data analytics. (Email: zhaoshijie@lntu.edu.cn)
  • Received Date: 2020-02-23
  • Accepted Date: 2020-08-11
  • Publish Date: 2021-01-01
  • To solve Multimodal optimization problems (MOPs), a Novel Quantum entanglement-inspired meta-heuristic framework (NMF-QE) is proposed. Its main inspirations are two concepts of quantum physics: quantum entanglement and quantum superposition. When given Proto-born particles (PBPs) of a population, these two concepts are mathematically developed to generate twin-born and combination-born particles, respectively. And if any elite-born particles would be created by a local re-searching strategy. These three or four groups of particles come together as a whole search population of NMF-QE to realize exploration and exploitation of algorithms. To guarantee dynamical optimization capability of NMF-QE, the individual evolutionary mechanism of some existing meta-heuristics will be adopted to iteratively create PBPs. A selected meta-heuristic is coupled with NMF-QE to present its improved variant. Numerical results show that the proposed NMF-QE can effectively improve optimization performance of meta-heuristics on MOPs.
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