This paper proposes the utilization of randomized backtracking within complete backtrack search algorithms for propositional satisfiability (SAT). In recent years, randomization has become pervasive in SAT algorithms. Incomplete algorithms for SAT, for example the ones based on local search, often resort to randomization. Complete algorithms also resort to randomization. These include state-of-...

Here we show how the idea of dynamic backtracking can be applied to branch and bound optimization. This is done by exploiting the concept of valued nogood introduced in [6, 16, 17]. However, simple replacement of nogoods with valued nogoods does not lead to a correct algorithm. We show that a way to achieve correctness is to use at each variable a separate nogood storage for each position that ...

In this paper we consider the optimal sequencing of vehicles along multiple assembly lines We present a constraint based model of the problem with hard and soft constraints An advantage of a constraint based approach is that the model is declarative and there is a separation between the model and an algorithm for solving the model As a result once the model had been de ned we could experiment w...

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...

Consider an infinite binary search tree in which the branches have independent random costs. Suppose that we must find an optimal (cheapest) or nearly optimal path from the root to a node at depth n. Karp and Pearl [1983] show that a bounded-lookahead backtracking algorithm A2 usually finds a nearly optimal path in linear expected time (when the costs take only the values 0 or 1). From this suc...

Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm; so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, rand...

In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Artificial Intelligence can be formalized as distributed CSPs. We pres...

Backtracking search is frequently applied to solve a constraint-based search problem but it often suuers from exponential growth of computing time. We present an alternative to backtracking search: local search based on connict minimization. We have applied this general search framework to study a benchmark constraint-based search problem, the n-queens problem. An eecient local search algorithm...

The minimum crossing number problem is among the oldest and most fundamental problems arising in the area of automatic graph drawing. In this paper, eight population-based meta-heuristic algorithms are utilized to tackle the minimum crossing number problem for two special types of graphs, namely complete graphs and complete bipartite graphs. A 2-page book drawing representation is employed for ...

The paper proposes an algorithm for solving the configuration-optimization coloring problem square (0,1)-matrices, which is based on method of selection their structural features. belongs to method's backtracking, however, construction optimal solution carried out among options that provide a certain configuration, due size search tree significantly reduced. A detailed scheme its operation give...