.png){kind=icon}
Return
Quantum-inspired space search algorithm (QSSA) for global numerical optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2011.07.067.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work presents the evolutionary quantum-inspired space search algorithm (QSSA) for solving numerical optimization problems. In the proposed algorithm, the feasible solution space is decomposed into regions in terms of quantum representation. As the search progresses from one generation to the next, the quantum bits evolve gradually to increase the probability of selecting the regions that render good fitness values. Through the inherent probabilistic mechanism, the QSSA initially behaves as a global search algorithm and gradually evolves into a local search algorithm, yielding a good balance between exploration and exploitation. To prevent a premature convergence and to speed up the overall search speed, an overlapping strategy is also proposed. The QSSA is applied to a series of numerical optimization problems. The experiments show that the results obtained by the QSSA are quite competitive compared to those obtained using state-of-the-art IPOP-CMA-ES and QEA. (C) 2011 Elsevier Inc. All rights reserved.
Keywords:
Quantum-inspired space search algorithm (QSSA)
Overlapping strategy
Numerical optimization
Evolutionary computation
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W
Organization
Cited Papers
ShareSaveShareSaveShareSaveComplementary and non-matching afferent compartments in the cat's superior colliculus: Innervation of the acetylcholinesterase-poor domain of the intermediate gray layer.png)
Neuroscience
NeuroscienceIF0
PREAI
ShareSave
View more