发表机构
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran（工业工程系，工程学院，卡扎米大学，德黑兰，伊朗）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基数约束下大规模投资组合优化问题，提出强化多目标进化算法的策略，引入独特解表示、新颖算子等，经测试该策略能提供更好近似解且收敛更快，性能随资产数增加无损失。
AI 中文摘要
由于金融市场中可供选择的投资越来越多，决策对投资者构成了日益严峻的挑战。过去几十年，投资组合优化一直是热门研究领域，旨在确定投资者在每种资产上的投资额。将现实条件引入优化模型会使其成为NP难问题，精确方法效率低下，因此研究人员转向进化算法来逼近解。本文提出了多目标进化算法的强化策略，在基数约束下的投资组合优化问题中能提供更快收敛速度和广泛搜索能力。为此引入了独特的解表示、新颖的算子和新的修复机制，并在著名的多目标进化算法中实施新的交配策略来解决该问题。随后使用著名市场指数作为基准，将定制算法与传统算法进行测试。结果表明，所提策略不仅能提供更好的近似解，收敛速度也更快，且随着市场中资产数量增加，性能无损失。
英文摘要
Decision-making is posing an increasingly formidable challenge to investors because of the growing number of alternatives available in financial markets. A hot area of research over the past few decades has been portfolio optimization that seeks to determine how much an investor should invest in which asset. Introducing real-world conditions to the optimization model turns the problem into an NP-hard one for whose solution exact methods become inefficient; hence, researchers have turned to evolutionary algorithms to approximate solutions. In this paper, strengthening strategies are presented for multi-objective evolutionary algorithms that can provide a faster convergence rate and extensive search ability in the portfolio optimization problem under the cardinality constraint. To implement those features, a unique solution representation, a novel operator, and new repair mechanisms are introduced for solving the aforementioned problem in which lower and upper limits are set on the number of assets in the portfolio. For this purpose, new mating strategies along with the aforesaid package are implemented in well-known multi-objective evolutionary algorithms to solve the problem. The customized algorithms are subsequently tested against traditional ones using well-known market indices as benchmarks. Results indicate that the proposed strategy not only provides better approximations but also converges faster as well at no loss of performance with an increasing number of assets in the market.
