105,497 research outputs found
AED: An Anytime Evolutionary DCOP Algorithm
Evolutionary optimization is a generic population-based metaheuristic that
can be adapted to solve a wide variety of optimization problems and has proven
very effective for combinatorial optimization problems. However, the potential
of this metaheuristic has not been utilized in Distributed Constraint
Optimization Problems (DCOPs), a well-known class of combinatorial optimization
problems prevalent in Multi-Agent Systems. In this paper, we present a novel
population-based algorithm, Anytime Evolutionary DCOP (AED), that uses
evolutionary optimization to solve DCOPs. In AED, the agents cooperatively
construct an initial set of random solutions and gradually improve them through
a new mechanism that considers an optimistic approximation of local benefits.
Moreover, we present a new anytime update mechanism for AED that identifies the
best among a distributed set of candidate solutions and notifies all the agents
when a new best is found. In our theoretical analysis, we prove that AED is
anytime. Finally, we present empirical results indicating AED outperforms the
state-of-the-art DCOP algorithms in terms of solution quality.Comment: 9 pages, 6 figures, 2 tables. Appeared in the proceedings of the 19th
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS
2020
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Event Scheduling
Distributed Constraint Optimization (DCOP) is an elegant formalism relevant to many areas in multiagent systems, yet complete algorithms have not been pursued for real world applications due to perceived complexity. To capably capture a rich class of complex problem domains, we introduce the Distributed Multi-Event Scheduling (DiMES) framework and design congruent DCOP formulations with binary constraints which are proven to yield the optimal solution. To approach real-world efficiency requirements, we obtain immense speedups by improving communication structure and precomputing best case bounds. Heuristics for generating better communication structures and calculating bound in a distributed manner are provided and tested on systematically developed domains for meeting scheduling and sensor networks, exemplifying the viability of complete algorithms. 1
Distributed Partitioned Big-Data Optimization via Asynchronous Dual Decomposition
In this paper we consider a novel partitioned framework for distributed
optimization in peer-to-peer networks. In several important applications the
agents of a network have to solve an optimization problem with two key
features: (i) the dimension of the decision variable depends on the network
size, and (ii) cost function and constraints have a sparsity structure related
to the communication graph. For this class of problems a straightforward
application of existing consensus methods would show two inefficiencies: poor
scalability and redundancy of shared information. We propose an asynchronous
distributed algorithm, based on dual decomposition and coordinate methods, to
solve partitioned optimization problems. We show that, by exploiting the
problem structure, the solution can be partitioned among the nodes, so that
each node just stores a local copy of a portion of the decision variable
(rather than a copy of the entire decision vector) and solves a small-scale
local problem
A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games
This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear
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