This work tackles two critical challenges related to the development of metaheuristics for Multi-Objective Optimization Problems (MOOPs): the exponential growth of non-dominated solutions and the tendency of metaheuristics to disproportionately concentrate their search on a subset of the Pareto Front. To counteract the first, bounded archives are employed as a strategic mechanism for effectively managing the increasing number of non-dominated solutions. Addressing the second challenge involves an in-depth exploration of solution diversity algorithms found in existing literature. Upon recognizing that current approaches predominantly center on diversity within the objective space, this research introduces innovative methods specifically designed to enhance diversity in the solution space. Results demonstrate the efficacy of the Hamming Distance Archiving Algorithm, one of the newly proposed algorithms for multi-objective local search, surpassing the performance of the Adaptive Grid Archiving and the Hypervolume Archiving, both drawn from the literature. This outcome suggests a promising avenue for enhancing the overall efficiency of metaheuristics employed for solving MOOPs.

翻译：本研究针对多目标优化问题元启发式算法开发中的两个关键挑战：非支配解的指数级增长以及元启发式算法倾向于将搜索过度集中于帕累托前沿的子集。为应对前者，有界归档被用作有效管理非支配解数量增长的战略机制。针对第二个挑战，本研究深入探讨了现有文献中的解多样性算法。在认识到当前方法主要聚焦于目标空间内多样性的基础上，本研究提出了专门用于增强解空间多样性的创新方法。结果表明，新提出的多目标局部搜索算法之一——汉明距离归档算法，其性能超越了文献中提取的自适应网格归档与超体积归档算法。这一结果为提升求解多目标优化问题的元启发式算法的整体效率提供了有前景的研究方向。