无参分组大规模变量的多目标算法研究

目前大多数多目标优化算法没有考虑到决策变量之间的交互性,只是将所有变量当作一个整体进行优化。随着决策变量的增加,多目标优化算法的性能会急剧下降。针对上述问题,提出一种无参变量分组的大规模变量的多目标优化算法(MOEA/DWPG)。该算法将协同优化与基于分解的多目标优化算法(MOEA/D)相结合,设计了一种不含参数的分组方式来提高交互变量分组的精确性,提高了算法处理含有大规模变量的多目标优化算法的性能。实验结果表明,该算法在大规模变量多目标问题上明显优于MOEA/D及其它先进算法。

A multi-objective optimization algorithm without parameter grouping and with large-scale variables

At present, most multi-objective optimization algorithms do not consider the interaction between decision variables, but just optimize all variables as a whole. With the increase of decision variables, the performance of multi-objective optimization algorithms will decrease sharply. Aiming at the above problems, a multi-objective optimization algorithm without parameter grouping and with large-scale variables (MOEA/DWPG) is proposed. By combining collaborative optimization with the decomposition-based multi-objective optimization algorithm (MOEA/D), this algorithm designs a grouping method without parameters to improve the grouping accuracy of interaction variables and to enhance the algorithm performance when it handles the multi-objective optimization problems with large-scale variables. Experimental results show that the proposed algorithm is significantly superior to MOEA/D and other advanced algorithms on the multi-objective problems with large-scale variables.