Quantum-inspired space search algorithm (QSSA) for global numerical optimization

Tzyy Chyang Lu, Jyh Ching Juang

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2516-2532
Number of pages17
JournalApplied Mathematics and Computation
Issue number6
Publication statusPublished - 2011 Nov 15

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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