The identification of decision variable interactions has a crucial role in the final outcome algorithm large-scale optimization domain. It is prerequisite for decomposition-based algorithms to achieve grouping. In this paper, we design recognition method with higher efficiency and grouping accuracy. based on decomposition strategy min hash solve global (LSGO) problems, called MHD. Our proposed ...

Abstract In this paper, we study arbitrary infinite binary information systems each of which consists an set called universe and two-valued functions (attributes) defined on the universe. We consider notion a problem over system, is described by finite number attributes mapping associating decision to tuple attribute values. As algorithms for solving, use deterministic nondeterministic trees. t...

In this paper, we propose a framework using local models for multi-objective optimization to guide the search heuristic in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We u...