论文标题
痣：通过多模式多目标景观挖掘隧道
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
论文作者
论文摘要
连续多模式多目标优化（MMMOO）景观的可视化最新进展为他们的搜索动力学带来了新的视角。在决策空间中很少隔离本地效率（LE）集，通常被视为本地搜索的陷阱。相反，通过超级吸引吸引盆地的相交导致至少部分包含更好解决方案的进一步解决方案集。多目标梯度滑动算法（MOGSA）是开发旨在利用这些叠加的算法概念。尽管它在线性LE集的许多MMMO问题上具有有希望的性能，但对MOGSA的仔细分析表明，它没有足够的推广到更广泛的测试问题。基于对MOGSA缺点的详细分析，我们提出了一种新算法，即多目标景观探索者（mole）。它能够有效地建模并利用MMMOO问题中的LE集。对双目标案例提出了摩尔的实施，该方法的实用性在双向BBOB测试的基准测试实验中显示。
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.
