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.

解决由时变优化目标函数和约束引起的动态优化问题 (DOP) 具有挑战性。近年来，为了加速此类优化问题的解决，量子计算受到了相当多的关注，并且已经开发出了量子优化算法。然而，目前还没有关于 DOP 量子优化算法的综合综述论文。本文对基于量子计算的动态优化进行了批判性回顾。首先给出动态优化问题的简要概述。详细介绍了现有的量子优化算法，包括QPSO、QEA、QAA、QGA、QNN等，并对不同量子算法的原理及其改进形式进行了介绍和讨论。不同量子优化算法得到的优化结果证明了量子算法相对于传统DOP优化算法的优越性。在不久的将来，应用于传统智能算法的量子计算仍然可以成为创新新算法的平台。基于量子学习算法的动态优化需要进一步研究。量子算法和量子计算机的终极结合仍然缺乏。该领域的发展可能会展现出解决动态优化问题的强大能力。