Traditional quantum-based evolutionary algorithms are intended to solve single-objective optimization problems or multiobjective small-scale optimization problems. However, multiobjective large-scale optimization problems are continuously emerging in the big-data era. Therefore, the research in this paper, which focuses on combining quantum mechanics with multiobjective large-scale optimization algorithms, will be beneficial to the study of quantum-based evolutionary algorithms. In traditional quantum-behaved particle swarm optimization (QPSO), particle position uncertainty prevents the algorithm from easily falling into local optima. Inspired by the uncertainty principle of position, the authors propose quantum-enhanced multiobjective large-scale algorithms, which are parallel multiobjective large-scale evolutionary algorithms (PMLEAs). Specifically, PMLEA-QDE, PMLEA-QjDE and PMLEA-QJADE are proposed by introducing the search mechanism of the individual particle from QPSO into differential evolution (DE), differential evolution with self-adapting control parameters (jDE) and adaptive differential evolution with optional external archive (JADE). Moreover, the proposed algorithms are implemented with parallelism to improve the optimization efficiency. Verifications performed on several test suites indicate that the proposed quantum-enhanced algorithms are superior to the state-of-the-art algorithms in terms of both effectiveness and efficiency.

传统的基于量子的进化算法旨在解决单目标优化问题或多目标小规模优化问题。但是，在大数据时代，多目标大规模优化问题不断出现。因此，本文的研究重点是将量子力学与多目标大规模优化算法相结合，将有助于基于量子的进化算法的研究。在传统的量子行为粒子群优化（QPSO）中，粒子位置不确定性会阻止算法轻易陷入局部最优状态。受位置不确定性原理的启发，作者提出了量子增强多目标大规模算法，该算法是并行多目标大规模进化算法（PMLEA）。具体而言，通过将来自QPSO的单个粒子的搜索机制引入微分进化（DE），具有自适应控制参数的微分进化（jDE）和带有可选参数的自适应微分进化，提出了PMLEA-QDE，PMLEA-QjDE和PMLEA-QJADE。外部档案（JADE）。此外，所提出的算法以并行方式实现，以提高优化效率。在多个测试套件上进行的验证表明，所提出的量子增强算法在有效性和效率上均优于最新算法。具有自适应控制参数（jDE）的差分演化和具有可选外部归档（JADE）的自适应差分演化。此外，所提出的算法以并行方式实现，以提高优化效率。在多个测试套件上进行的验证表明，所提出的量子增强算法在有效性和效率上均优于最新算法。具有自适应控制参数（jDE）的差分演化和具有可选外部归档（JADE）的自适应差分演化。此外，所提出的算法以并行方式实现，以提高优化效率。在多个测试套件上进行的验证表明，所提出的量子增强算法在有效性和效率上均优于最新算法。