Financial technology and smart transportation is key cross-field of transportation in the future. The demand for smart transportation investment is constantly released. As typical and efficient financial products, asset-backed securities (ABS) can greatly improve the turnover efficiency of funds between upstream suppliers and downstream buyers in the field of smart transportation and also help participants of the supply chain to maintain healthier financial situations. However, one of the most common problems of ABS is portfolio allocation, which needs portfolio optimization based on massive assets with multiple objectives and constraints. Especially, in the field of smart transportation, sources of underlying assets can always be complex, which may involve a variety of subdivision industries and regions. At the same time, due to the relationships between upstream and downstream entities in the supply chain, correlations among assets can be strong. So, during the optimization of smart transportation ABS portfolio allocation, it is necessary to identify and deal with those problems. Different from forward selection or linear optimization, which could have low efficiency for complicated problems with large sample size and multiple objectives, new methods and algorithms for NP-hard problems would be necessary to be investigated. In this article, a penalty function based on graph density (GD) was introduced to the particle swarm optimization algorithm (PSO), and a GD-PSO algorithm was proposed. Experiments also showed that the GD-PSO algorithm solved the problem of portfolio optimization in smart transportation supply chain ABS effectively.

中文翻译：

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智能运输供应链ABS资产组合优化的GD-PSO算法

金融技术和智能交通是未来交通运输的关键领域。对智能交通投资的需求正在不断释放。作为典型且高效的金融产品，资产支持证券（ABS）可以大大提高智能交通领域上游供应商和下游购买者之间资金的周转效率，还可以帮助供应链参与者保持更健康的财务状况。然而，获取和惠益分享最常见的问题之一是投资组合分配，这需要基于具有多个目标和约束的大规模资产来优化投资组合。特别是在智能交通领域，基础资产的来源总是很复杂，可能涉及各种细分行业和地区。同时，由于供应链中上游和下游实体之间的关系，资产之间的相关性可能很强。因此，在优化智能交通ABS资产组合分配过程中，有必要识别和处理这些问题。与前向选择或线性优化不同，前向选择或线性优化可能会降低样本量大，目标多的复杂问题的效率，因此有必要研究解决NP难问题的新方法和算法。本文将基于图密度（GD）的惩罚函数引入粒子群优化算法（PSO），提出了一种GD-PSO算法。实验还表明，GD-PSO算法有效地解决了智能运输供应链ABS中的资产组合优化问题。