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ОглавлениеRH-Quantum Portfolio (2026), NSGA – описать подробно, перечислить литературу
NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a popular multi-objective evolutionary optimization algorithm used in portfolio optimization to balance risk and return. «RH-Quantum Portfolio (2026)» likely refers to a quantum-inspired or hybrid portfolio optimization approach projected for 2026, possibly incorporating NSGA-II with quantum annealing or variational methods for enhanced performance in financial modeling. [1] [2] [3]
Core Concept
NSGA-II ranks solutions by non-domination levels and crowding distance to generate Pareto-optimal portfolios, outperforming classical methods like Markowitz in handling constraints such as semi-variance, liquidity, and cardinality. [2] [4] [5] In quantum contexts, it hybridizes with algorithms like QAOA or VQE for complex, higher-order moment optimizations (e.g., skewness, kurtosis) relevant to 2026 quantum hardware advances. [6] [7] [3] This suits 2026 portfolios amid rising quantum computing stocks like D-Wave (QBTS) and IonQ (IONQ), which emphasize optimization for finance. [8] [1]
Key Applications
– Risk-Return Trade-off: NSGA-II yields portfolios with superior returns (e.g., 41.25% annualized) and lower risk than baselines, tested on indices like Tehran Stock Exchange or S&P100. [2] [4]
– Quantum Integration: D-Wave’s annealing optimizes 60-stock portfolios; hybrids with NSGA-II target NISQ-era limits for 2026 commercial use in finance. [9] [1]
– 2026 Relevance: Aligns with forecasts for fault-tolerant systems enabling real-world portfolio tools, reducing computation for combinatorial problems. [10] [11]
Literature
– Portfolio Optimization Utilizing Multi-Objective NSGA-II (SSRN, 2024): Applies NSGA-II to 132 Tehran stocks, showing better risk-return profiles. [2]
– Design of Optimum Portfolio Scheme Based on Improved NSGA-II (PMC, 2022): Improves convergence for financial multi-objectives, achieving 70.08% predicted profits. [4]
– Multiobjective Approach to Portfolio Optimization with NSGA-II (Vilnius Tech, 2020): Uses credibilistic ratios for fuzzy risk-return-liquidity models on S&P100. [5]
– Quantum Portfolio Optimization with Expert Analysis (arXiv, 2025): Benchmarks VQE/QAOA, relevant for NSGA hybrids. [3]
– On NSGA-II and NSGA-III in Portfolio Management (TechScience, 2021): Compares genetic algorithms for constrained models. [12]
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Подробный разбор шагов алгоритма NSGA-II
NSGA-II работает в поколениях, комбинируя элитизм, быструю недоминирующую сортировку и crowding distance для отбора. Алгоритм минимизирует несколько целей, поддерживая разнообразие на фронтах Парето. [51] [52]
