Local search is widely applied to satisfiable SAT problems, and on some classes outperforms backtrack search. An intriguing challenge posed by Selman, Kautz and McAllester in 1997 is to use it instead to prove unsatisfiability. We investigate two distinct approaches. Firstly we apply standard local search to a reformulation of the problem, such that a solution to the reformulation corresponds t...

Iterated Local Search has many of the desirable features of a metaheuristic: it is simple, easy to implement, robust, and highly effective. The essential idea of Iterated Local Search lies in focusing the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for a given optimization engine. The success of Iterated Local Search lies...

This paper describes a hybrid approach to solving large-scale constraint satisfaction and optimization problems. It describes a hybrid algorithm for integer linear programming which combines local search and bounds propagation, inspired by the success of a randomized algorithm for Boolean Satisfiability (SAT) called Unit-Walk. A dynamic prioritization heuristic has been developed to improve the...

Boundary-spanning search has been argued to be important for the success of innovation. There are various kinds of dimensions about organizational search activities. Past studies on boundary-spanning search have focused mainly on technological dimension. We characterize the boundary-spanning search on geographic dimension. We propose a conceptual model and 6 hypotheses. Data from 156 firms were...

In this paper we introduce a technique for building eecient iterated local search procedures. This technique, that we call perturbation, uses global information on TSP instances to speed-up and improve the quality of the tours found by heuristic methods. The main idea is to escape from local optima by introducing perturbations in the problem instance rather than in the solution. The performance...

The basic idea of local search is to find a good local optima quickly enough to approximate the global optima. This idea is somewhat like hill climbing. Starting from an arbitrary point, we try to go down or climb up a little step and compare the objective value at the new point to that at the old point. We then choose the step that increase the objective value. In this manner, we finally reach...

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This paper describes a two-tiered approach to the self-localization problem for soccer playing robots using generic off-the-shelf color cameras. The solution consists of two layers; the top layer is a global search assuming zero knowledge, and the bottom layer is a local search, assuming a relatively good estimation of the position and orientation of the robot. The global search generally yield...