Abstract-The Distributed Stochastic Algorithm (DSA), Distributed Breakout Algorithm (DBA), and variations such as Distributed Simulated Annealing (DSAN), MGM-1, and DisPeL, are distributed hill-climbing techniques for solving large Distributed Constraint Optimization Problems (DCOPs) such as distributed scheduling, resource allocation, and distributed route planning. Like their centralized counterparts, these algorithms employ escape techniques to avoid getting trapped in local minima during the search process. For example, the best known version of DSA, DSA-B, makes hill-climbing and lateral escape moves, moves that do not impact the solution quality, with a single probability p. DSAN uses a similar scheme, but also occasionally makes a move that leads to a worse solution in an effort to find a better overall solution. Although these escape moves tend to lead to a better solutions in the end, the cost of employing the various strategies is often not well understood. In this work, we investigate the costs and benefits of the various escape strategies by empirically evaluating each of these protocols in distributed graph coloring and sensor tracking domains. Through our testing, we discovered that by reducing or eliminating escape moves, the cost of using these algorithms decreases dramatically without significantly affecting solution quality.
Abstract. Traffic congestion is a widespread epidemic that continually wreaks havoc in urban areas. Traffic jams, car wrecks, construction delays, and other causes of congestion, can turn even the biggest highways into a parking lot. Several congestion mitigation strategies are being studied, many focusing on micro-simulation of traffic to determine how modifying road structures will affect the flow of traffic and the networking perspective of vehicle-to-vehicle communication. Vehicle routing on a network of roads and intersections can be modeled as a distributed constraint optimization problem and solved using a range of centralized to decentralized techniques. In this paper, we present a constraint optimization model of a traffic routing problem. We produce congestion data using a sinusoidal wave pattern and vary its amplitude (saturation) and frequency (vehicle waves through a given intersection). Through empirical evaluation, we show how a centralized and decentralized solution each react to unknown congestion information that occurs after the initial route planning period.
Distributed hill-climbing algorithms are a powerful, practical technique for solving large Distributed Constraint Satisfaction Problems (DSCPs) such as distributed scheduling, resource allocation, and distributed optimization. Although incomplete, an ideal hill-climbing algorithm finds a solution that is very close to optimal while also minimizing the cost (i.e. the required bandwidth, processing cycles, etc.) of finding the solution. The Distributed Stochastic Algorithm (DSA) is a hill-climbing technique that works by having agents change their value with probability p when making that change will reduce the number of constraint violations. Traditionally, the value of p is constant, chosen by a developer at design time to be a value that works for the general case, meaning the algorithm does not change or learn over the time taken to find a solution. In this paper, we replace the constant value of p with different probability distribution functions in the context of solving graph-coloring problems to determine if DSA can be optimized when the probability values are agent-specific. We experiment with non-adaptive and adaptive distribution functions and evaluate our results based on the number of violations remaining in a solution and the total number of messages that were exchanged.
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