Abstract: To solve the complex and diverse matching problems between enterprises financial products in the industrial chain,an improved discrete particle swarm optimization algorithm based on 0-1 knapsack strategy was proposed from the perspective of risk tolerance and financing efficiency for solving the bilateral matching model.The objective function was constructed aiming at the minimum risk tolerance requirement of enterprise and the highest financing efficiency of financial products.The highest preference sequence information was used to calculate the bilateral match main body satisfaction,and the bilateral matching optimization model which was calculated by improved discrete particle swarm algorithm was built.The updated iterative calculation way of particle swarm velocity and displacement were simplified by this method,the algorithm redundancy was reduced efficiently,and the solution convergence speed and precision of the model were improved.The new energy automobile industry chain was used to verify the calculation speed and searching ability of the algorithm,and the result showed that the operation speed and the searching ability of the improved algorithm was proved greatly by compared with the traditional algorithm,and the defects of premature convergence and slow convergence speed were avoided.

Key words: industry chain finance, 0-1 knapsack strategy, discrete particle swarm optimization algorithm, risk bearing capacity, financing efficiency

摘要： 为解决产业链上各节点企业与金融产品复杂多样的匹配问题,从风险承受力与融资效率视角出发,提出基于0-1背包策略改进离散粒子群算法对双边匹配模型进行求解。以企业风险承受能力要求最小、融资效率最高为目标函数,利用偏好序信息计算出双边匹配主体的满意度,构建了双边匹配优化模型,并运用改进离散粒子群算法进行求解。该方法简化了粒子群速度和位移的更新迭代计算方式,有效地减少了算法冗余性,提高了模型求解的收敛速度和精度。以新能源汽车产业链为例验证了算法的运算速度和寻优能力,并与传统算法比较,结果表明改进算法的运算速度和寻优能力都得到较大的提高,较好地避免了过早收敛和收敛速度慢的缺陷。

关键词: 产业链金融, 0-1背包策略, 离散粒子群优化算法, 风险承受力, 融资效率