期刊
NEURAL COMPUTING & APPLICATIONS
卷 31, 期 7, 页码 2763-2780出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-017-3228-9
关键词
Quantum computing (QC); Multiverse optimization (MVO); Numerical optimization; Evolutionary algorithms (EAs)
In this paper, a new hybrid algorithm called quantum multiverse optimization (QMVO) is proposed. The proposed QMVO is based on quantum computing and multiverse optimization (MVO) algorithm. The main features of quantum theory and MVO were applied in a new algorithm to find the optimal trade-off between exploration and exploitation. QMVO algorithm depends on adopting a quantum representation of the search space and the integration of the quantum interference and operators in the multiverse optimization algorithm to obtain the optimal solution of the objective function. The performance of QMVO algorithm is evaluated by using 50 unimodal and multimodal benchmark functions. The experimental results show that the proposed algorithm has comprehensive superiority in solving complex numerical optimization problems. Also, the results show that the proposed QMVO is a promising optimization algorithm compared with other well-known and popular algorithms.
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