CLMOAS: collaborative large-scale multi-objective optimization algorithms with adaptive strategies

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of decision variables. However, in practical applications, complex optimization problems often involve multiple objectives and large-scale decision variables. To address these challenges, this paper proposes an innovative large-scale multi-objective evolutionary optimization algorithm. The algorithm utilizes clustering techniques to categorize decision variables and introduces a novel dominance relation to enhance optimization efficiency and performance. By dividing decision variables into convergence-related and diversity-related groups and applying distinct optimization strategies to each, the algorithm achieves a better balance between convergence and diversity. Additionally, the algorithm incorporates a new angle-based dominance relationship to reduce dominance resistance during the optimization process. Experimental results on multiple mainstream multi-objective optimization test sets, such as standard DTLZ and UF problem sets, indicate that CLMOAS achieves smaller IGD values relative to mainstream algorithms such as MOEA/D and LMEA, thereby demonstrating that the proposed algorithm outperforms several existing multi-objective evolutionary algorithms and showcases its effectiveness in solving complex optimization problems with multiple objectives and large-scale decision variables.