多策略大规模多目标优化算法

Large-scale Multi-objective Optimization Algorithm with Multiple Strategies

在解决大规模多目标优化问题(LSMOP)时,随着决策变量维数的增加会使得MOEA/D算法在决策空间扩展性差且容易收敛于局部最优.针对这一问题,提出了一种大规模多策略MOEA/D算法(MSMOEA/D). MSMOEA/D算法在优化过程中引入了一种基于自动编码器的混合初始化策略,以改善初始种群的覆盖程度,从而促进全局搜索.然后,提出一种基于聚合函数值的邻域调整策略,通过调整邻域大小,能够在搜索过程中更精确地控制搜索范围,避免因邻域过大或过小而导致的搜索效率低下.此外,在优化过程中采用了基于非支配排序的变异选择策略.不同的子问题根据位于非支配排序第1层的个体数量选择变异策略,避免种群陷入局部最优,提高算法的整体性能.最后,使用LSMOP和DTLZ测试问题对MSMOEA/D算法和其他已有算法进行了评估.实验结果证实了MSMOEA/D算法解决大规模多目标优化问题的有效性.

When dealing with large-scale multi-objective optimization problem(LSMOP),the MOEA/D algorithm shows poor scalability in the decision space and a tendency to converge to local optima as the dimensionality of decision variables increases.To address this issue,this study proposes a large-scale MOEA/D algorithm with multiple strategies(MSMOEA/D).The MSMOEA/D algorithm introduces a hybrid initialization strategy based on autoencoders in the optimization process to expand the coverage of the initial population,thus promoting global search.Moreover,a neighborhood adjustment strategy based on aggregation functions is proposed,which can more accurately control the search range during the search process by adjusting neighborhood sizes,thereby avoiding low search efficiency caused by excessively large or small neighborhoods.Furthermore,a mutation-selection strategy based on non-dominated sorting is adopted during the optimization process.Different subproblems select their mutation strategies according to the number of individuals in the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm.Finally,the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems.Experimental results verify the effectiveness of the proposed algorithm for solving LSMOPs.