Multi-objectivization is a term used to describe strategies developed for optimizing single-objective problems by multi-objective algorithms. This paper focuses on multi-objectivizing the sum-of-the-parts combinatorial optimization problems, which include the traveling salesman problem, the unconstrained binary quadratic programming and other well-known combinatorial optimization problem. For a sum-of-the-parts combinatorial optimization problem, we propose to decompose its original objective into two sub-objectives with controllable correlation. Based on the decomposition method, two new multi-objectivization inspired single-objective optimization techniques called non-dominance search and non-dominance exploitation are developed, respectively. Non-dominance search is combined with two metaheuristics, namely iterated local search and iterated tabu search, while non-dominance exploitation is embedded within the iterated Lin-Kernighan metaheuristic. The resultant metaheuristics are called ILS+NDS, ITS+NDS and ILK+NDE, respectively. Empirical studies on some TSP and UBQP instances show that with appropriate correlation between the sub-objectives, there are more chances to escape from local optima when new starting solution is selected from the non-dominated solutions defined by the decomposed sub-objectives. Experimental results also show that ILS+NDS, ITS+NDS and ILK+NDE all significantly outperform their counterparts on most of the test instances.

多目标化是一个术语，用于描述为通过多目标算法优化单目标问题而开发的策略。本文着重于多目标化零件总和组合优化问题，包括旅行商问题，无约束二进制二次规划以及其他众所周知的组合优化问题。对于零件总和组合优化问题，我们建议将其原始目标分解为具有可控相关性的两个子目标。基于分解方法，两种新的多目标启发单目标优化技术称为非优势搜索和非优势开发。分别开发。非优势搜索与两种元启发式算法相结合，即迭代局部搜索和迭代禁忌搜索，而非优势开发则嵌入在迭代的Lin-Kernighan元启发式方法中。产生的元启发法分别称为ILS + NDS，ITS + NDS和ILK + NDE。对某些TSP和UBQP实例的经验研究表明，在子目标之间具有适当的相关性时，从分解的子目标定义的非支配解中选择新的起始解时，有更多的机会摆脱局部最优。实验结果还表明，在大多数测试实例中，ILS + NDS，ITS + NDS和ILK + NDE均明显优于同类产品。