High-dimensional portfolio optimization faces significant computational challenges under complex constraints, with traditional optimization methods struggling to balance convergence speed and global exploration capability. To address this, firstly, we introduce an enhanced Sharpe ratio-based model that incorporates all constraints into the objective function using adaptive penalty terms, transforming the original constrained problem into an unconstrained single-objective formulation. This approach preserves financial interpretability while simplifying algorithmic implementation. To efficiently solve the resulting high-dimensional optimization problem, we develop a Quantum Hybrid Differential Evolution (QHDE) algorithm, which introduces a dynamic quantum tunneling mechanism that enables individuals to probabilistically escape local optima, dramatically enhancing global exploration and solution flexibility. To further improve performance, a good point set-chaos reverse learning strategy generates a well-dispersed initial population, providing a robust and diverse starting point. Meanwhile, a dynamic elite pool combined with Cauchy-Gaussian hybrid perturbations maintains population diversity and mitigates premature convergence, ensuring stable and high-quality solutions. Experimental validation on CEC benchmarks and real-world portfolios involving 20 to 80 assets demonstrates that QHDE's performance improves by up to 96.6%. It attains faster convergence, higher solution precision, and greater robustness than seven state-of-the-art counterparts, thereby confirming its suitability for complex, high-dimensional portfolio optimization.

翻译：高维投资组合优化在复杂约束条件下面临显著的计算挑战，传统优化方法难以平衡收敛速度与全局探索能力。针对这一问题，我们首先提出一种增强型夏普比率模型，该模型通过自适应惩罚项将所有约束纳入目标函数，将原始约束问题转化为无约束单目标形式。该方法在保持金融可解释性的同时简化了算法实现。为高效求解由此产生的高维优化问题，我们开发了量子混合差分进化算法，该算法引入动态量子隧穿机制，使个体能够概率性地跳出局部最优，显著增强了全局探索能力与解空间灵活性。为进一步提升性能，采用基于佳点集-混沌反向学习策略生成分布均匀的初始种群，提供稳健且多样化的起点。同时，结合动态精英池与柯西-高斯混合扰动，维持种群多样性并抑制早熟收敛，确保解的质量与稳定性。基于CEC基准测试及包含20至80种资产的真实投资组合的实验验证表明，QHDE的性能提升最高达96.6%，其在收敛速度、解精度和鲁棒性方面均优于七种当前最先进算法，证实了其适用于复杂高维投资组合优化问题。