Recent advances in the visualization of continuous multimodal multi-objective optimization (MMMOO) landscapes brought a new perspective to their search dynamics. Locally efficient (LE) sets, often considered as traps for local search, are rarely isolated in the decision space. Rather, intersections by superposing attraction basins lead to further solution sets that at least partially contain better solutions. The Multi-Objective Gradient Sliding Algorithm (MOGSA) is an algorithmic concept developed to exploit these superpositions. While it has promising performance on many MMMOO problems with linear LE sets, closer analysis of MOGSA revealed that it does not sufficiently generalize to a wider set of test problems. Based on a detailed analysis of shortcomings of MOGSA, we propose a new algorithm, the Multi-Objective Landscape Explorer (MOLE). It is able to efficiently model and exploit LE sets in MMMOO problems. An implementation of MOLE is presented for the bi-objective case, and the practicality of the approach is shown in a benchmarking experiment on the Bi-Objective BBOB testbed.
翻译:连续多式联运多目标优化(MMMOO)景观的视觉化最近的进展为其搜索动态带来了新的视角。当地高效(LE)数据集,通常被视为本地搜索的陷阱,很少在决策空间中被孤立。相反,通过叠加吸引盆地的交叉导致更多的解决方案组合,至少部分包含更好的解决方案。多目标增速滑动算法(MOGSA)是一个为利用这些叠加位置而开发的算法概念。虽然它在许多线性LE集的MMMOO问题上表现良好,但对MOGSA的更仔细分析表明,它没有足够概括到更广泛的测试问题。根据对MOGSA缺陷的详细分析,我们提出了新的算法,即多目标地貌探索器(MOLE),它能够有效地模拟和利用MMMMOO问题中的LE。双目标案例介绍了MOLE的落实情况,而在双目标BBBBBB试验台进行的基准实验中展示了这种方法的实用性。