随机扰动不同种群变异策略的多目标进化算法

为了提高多目标优化问题非支配解集的收敛性和多样性，解决算法后期易陷入局部最优的问题，根据不同差分进化策略特点，添加随机扰动，基于改进切比雪夫机制提出了一种自适应差分进化策略的分解多目标进化算法（MOEA/D-ADE-levy）。首先使用混合水平正交实验产生均匀权重向量并应用于改进切比雪夫机制分解子问题得到均匀分布的初始种群；其次将种群分为优秀个体、中间个体和较差个体，对不同个体采用不同的变异策略，对变异因子F和交叉概率CR采用自适应机制，提高非支配解集的收敛性和多样性；最后对陷入局部最优的解集增加levy随机扰动，增大其全局搜索的能力，跳出局部最优。采用DTLZ测试函数验证算法有效性，将所提算法与NSGA2、NSGA3、MOEA\D、MOEA\D-DE等常用算法进行比较，使用GD和IGD评价指标对算法进行多样性和收敛性分析，实验结果表明，该算法在收敛性和多样性方面得到了改进与提高，能得到更优的Pareto解集。

Multi-objective evolutionary algorithm with random disturbance of different population mutation strategies

In order to improve the convergence and diversity of the non-dominated solution set of multi-objective optimization problems, and solve the problem that the algorithm is easy to fall into the local optimum in the later stage, according to the characteristics of different differential evolution strategies, this paper added random disturbances, and proposed a decomposition multi-objective evolution algorithm with adaptive differential evolution strategy based on the improved Chebyshev mechanism(MOEA/D-ADE-levy). Firstly, it used the mixed-level orthogonal experiment to generate uniform weight vectors and applied to improve the Chebyshev mechanism to decompose the sub-problems to obtain a uniformly distributed initial population. Secondly, it divided the population into excellent individuals, intermediate individuals and poor individuals, and used different individuals. The mutation strategy used an adaptive mechanism for the mutation factor F and the crossover probability CR to improve the convergence and diversity of the non-dominated solution set. Finally, it added the levy random perturbation to the solution set that falled into the local optimum to increase its global search ability. Jump out of the local optimum. The DTLZ test function was used to verify the effectiveness of the algorithm. And the proposed algorithm was compared with common algorithms such as NSGA2, NSGA3, MOEA\D, MOEA\D-DE, etc.. And the diversity and convergence analysis of the algorithm was performed using GD and IGD evaluation indicators. The results show that the algorithm has been improved and improved in terms of convergence and diversity, and can obtain a better Pareto solution set.