Abstract
Solving dynamic optimization problems (DOPs) induced by time-varying optimization objective functions and constraints is challenging. Quantum computation has received considerable attention to accelerate the solution of such optimization problems in recent years, and quantum optimization algorithms have been developed. Yet, there are no comprehensive review papers on quantum optimization algorithms for DOPs. This paper presents a critical review of dynamic optimization based on quantum computing. A brief overview of dynamic optimization problems is first given. The existing quantum optimization algorithms are introduced in detail, including QPSO, QEA, QAA, QGA, QNN, etc. The principles of different quantum algorithms and their improved forms are presented and discussed. The optimization results obtained by different quantum optimization algorithms prove the superiority of quantum algorithms over traditional optimization algorithms for DOPs. In the near future, quantum computing applied to traditional intelligent algorithms can still be the platform for innovating new algorithms. The dynamic optimization based on quantum learning algorithms needs further research. The ultimate combination of quantum algorithms and quantum computers is still lacking. Developments in this area may show powerful capabilities to solve dynamic optimization problems.
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