多目标伪布尔优化的高效爬山算法
Efficient Hill-Climber for Multi-Objective Pseudo-Boolean Optimization
摘要：
局部搜索算法和迭代局部搜索算法是一种基本技术。局部搜索可以是一种独立的搜索方法，但它也可以与进化算法混合。最近，它已被证明，它是可能的，以确定在汉明邻域的k-有界伪布尔优化问题在常数时间的改进移动。这意味着局部搜索不需要枚举邻域来找到改进的移动。这也意味着进化算法不需要使用随机变异作为算子，除非是作为一种逃避局部最优的方法。在本文中，我们将展示如何提高移动可以确定在常数时间的多目标问题，表示为k-有界伪布尔函数。特别是，多目标形式的NK景观和MK景观被认为是。
英文摘要：
Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand along search methods, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in constant time. This means that local search does not need to enumerate neighborhoods to find improving moves. It also means that evolutionary algorithms do not need to use random mutation as a operator, except perhaps as a way to escape local optima. In this paper, we show how improving moves can be identified in constant time for multiobjective problems that are expressed as k-bounded pseudo-Boolean functions. In particular, multiobjective forms of NK Landscapes and Mk Landscapes are considered.
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