A Novel Quantum Entanglement‐Inspired Meta‐heuristic Framework for Solving Multimodal Optimization Problems. Issue 1 (1st January 2021) - Shijie, Zhao; Shilin, Ma; Leifu, Gao; Dongmei, Yu
Abstract : 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.