An improved DPOP algorithm based on breadth first search pseudo-tree for distributed constraint optimization

Chen, Ziyu; He, Zhen; He, Chen

doi:10.1007/s10489-017-0905-4

Cited by 13 publications

(16 citation statements)

References 27 publications

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“…PCD is a PSO-based iterative algorithm that first constructs a Breadth First Search (BFS) pseudo-tree [28], which orders the agents, in a preprocessing step. Figure 2 illustrates a BFS pseudo-tree constructed from the constraint graph shown in Figure 1 having x 1 4 as the root.…”

Section: The Pcd Algorithmmentioning

confidence: 99%

A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

Choudhury

Mahmud

Khan

2020

AAAI

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Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of several distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F-DCOPs) have been proposed that can explicitly model a problem containing continuous variables. Nevertheless, state-of-the-art F-DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state-of-the-art approaches in terms of solution quality and computation overhead.

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Section: The Pcd Algorithmmentioning

confidence: 99%

A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

Choudhury

Mahmud

Khan

2020

AAAI

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“…This condition greatly reduces the algorithm's performance. To deal with this drawback, [2] proposes the BFS-DPOP variant which uses the Breadth-First Search (BFS) pseudo-tree as the communication structure.…”

Section: Background and Problem Formulationmentioning

confidence: 99%

“…Therefore, it is worth noting that similar to the aforementioned DPOP extensions, BrC-DPOP uses a depth-first search pseudo tree to graphically represent a DCOP. Recently, it has been shown that this approach often results in a chain-like structure that impairs the performance of the algorithm due to the lack of parallelism [2]. Nevertheless, the algorithm proposed in the paper, the so-called BFS-DPOP, shows the significance of an alternative graphical representation − a breadthfirst search pseudo tree.…”

Section: Introductionmentioning

confidence: 99%

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Speeding up distributed pseudo-tree optimization procedures with cross edge consistency to solve DCOPs

et al. 2020

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The Distributed Pseudo-tree Optimization Procedure (DPOP) is a well-known message passing algorithm that provides optimal solutions to Distributed Constraint Optimization Problems (DCOPs) in cooperative multi-agent systems. However, the traditional DCOP formulation does not consider constraints that must be satisfied (hard constraints), rather it concentrates only on constraints that place no restriction on satisfaction (soft constraints). This is a serious shortcoming as many realworld applications involve both types of constraints. Traditional DPOP algorithms are not able to benefit from the existence of hard constraints, where an additional calculation is required to handle such constraints. This results in longer runtimes. Thus scalability remains an issue. Additionally, in the standard DPOP, the agents are arranged as a Depth First Search (DFS) pseudo-tree, but recent work has shown that the construction of pseudo-trees in this way often leads to chain-like communication structures that greatly impair the algorithm's performance. To address these issues, we develop an algorithm that speeds up the DPOP algorithm by reducing the size of the messages exchanged and increases parallelism in the pseudo tree. For this purpose, initially, we improve the path for exchanging messages. Next, we introduce a new form of constraint propagation, which we call cross-edge consistency. Our theoretical evaluation shows that our proposed algorithm is complete and correct. In empirical evaluations, our algorithm achieves a significant reduction in the runtime, ranging from 4% to 96%, compared to the state-of-the-art.

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“…We suggest using the algorithm described in(Chen, He, and He 2017). Height can be easily calculated by utilizing LAYER information.3 Length of the longest path in the pseudo-tree.4 AED takes a default value for each of the parameters as input.…”

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confidence: 99%

AED: An Anytime Evolutionary DCOP Algorithm

Mahmud,

Choudhury,

Khan

et al. 2019

Preprint

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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 populationbased 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.

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An improved DPOP algorithm based on breadth first search pseudo-tree for distributed constraint optimization

Cited by 13 publications

References 27 publications

A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

Speeding up distributed pseudo-tree optimization procedures with cross edge consistency to solve DCOPs

AED: An Anytime Evolutionary DCOP Algorithm

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