Open AccessA Novel Quantum Entanglement‐Inspired Meta‐heuristic Framework for Solving Multimodal Optimization Problems
Author(s) -
Shijie Zhao,
Shilin Ma,
Leifu Gao,
Dongmei Yu
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.11.012
Subject(s) - quantum entanglement , heuristics , computer science , heuristic , mathematical optimization , quantum , optimization problem , population , theoretical computer science , algorithm , mathematics , quantum mechanics , physics , demography , sociology
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.