极大极小距离密度多目标微分进化算法在投资组合优化中的应用

his paper introduces the multi-objective Differential Evolution based on max-min distance destiny to solve the multi-objective portfolio optimization model. The standard Differential Evolution algorithm is not applicable to solve the multi-objective optimization, this improved multi-objective Differential Evolution algorithm employing the max-min distance destiny to stand for the density of individual, redefines the Selection operation of Differential Evolution algorithm and the maintenance regulation of the Pareto candidate solution set, which enables the algorithm to ensure diversity of the Pareto solution set and the convergence of the algorithm. After building a Mean-Variance Portfolio Bi-objective Optimization model, this study considers the skewness as well, builds Mean-Variance-Skewness Tri-objective Portfolio Optimization model. This paper chooses the yield of 100 Growth Enterprise Market stocks, the experimental results indicate that the multi-objective Differential Evolution could be able to solve the multi-objective portfolio optimization effectively, the Pareto Optimal set of the Tri-objective portfolio model could provide more references for investors, and it is feasible to introduce this algorithm to other multi-objective portfolio optimization models.