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Quantum multiverse optimization algorithm for optimization problems

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Abstract

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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Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Gehad Ismail Sayed & Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  3. Scientific Research Group in Egypt, Cairo, Egypt

    Gehad Ismail Sayed, Ashraf Darwish & Aboul Ella Hassanien

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  1. Gehad Ismail Sayed
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  2. Ashraf Darwish
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  3. Aboul Ella Hassanien
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Correspondence to Gehad Ismail Sayed.

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We declare that we have no significant competing financial, professional or personal interests that might have influenced the performance or presentation of the work described in this manuscript.

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Sayed, G.I., Darwish, A. & Hassanien, A.E. Quantum multiverse optimization algorithm for optimization problems. Neural Comput & Applic 31, 2763–2780 (2019). https://doi.org/10.1007/s00521-017-3228-9

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  • DOI: https://doi.org/10.1007/s00521-017-3228-9

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