Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver

Fransman, Jeroen; Sijs, Joris; Dol, Henry; Theunissen, E.; Schutter, Bart De

doi:10.1613/jair.1.14151

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…Since continuous nonlinear optimization methods such as gradient descent require derivative computations, HCMS is difficult to solve non-differentiable problems and cannot guarantee convergence. B-DPOP [12] extends DPOP algorithm by adding Bayesian optimization and Gaussian process models to solve dynamic coordination problems in continuous domains. It converges to optimal solution in fewer sampling iterations, but has high computational complexity requiring significant computing resources and time for larger problems.…”

Section: Introductionmentioning

confidence: 99%

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An Estimation of Distribution Based Algorithm for Continuous Distributed Constraint Optimization Problems

Shi,

Zhang,

Liao

et al. 2024

ITC

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Continuous Distributed Constraint Optimization Problem(C-DCOP) is a constraint processing framework for continuous variables problems in multi-agent systems. There is a constraint cost function between two mutually restrictive agents in C-DCOP. The goal of the C-DCOP solving algorithm is to keep the sum of constraint cost functions in an extreme state. In a C-DCOP, each function is defined by a set of continuous variables. At present, some C-DCOP solving algorithms have been proposed, but there are some common problems such as the limitation of constraints cost function form, easy to fall into local optimum, and lack of anytime attribute. Aiming at these thorny problems, we propose a parallel optimization algorithm named Estimation of Distribution Based Algorithm for Continuous Distributed Constraint Optimization Problems (EDA-CD). In EDA-CD, each solution is regarded as an individual, and the distribution of agent value is jointly described by all outstanding individuals. Firstly, all agents cooperate to hold a distributed population. Secondly, each agent calculates the mean and variance of its variables to build probability models in parallel. Finally, the agent evaluates the fitness of samples and updates the probability model through cooperative communication on Breadth First Search (BFS) pseudo-tree. We theoretically prove that EDA-CD is an anytime algorithm. The extensive experimental results on four types of benchmark problems show that the proposed EDA-CD outperforms the state-of-the-art C-DCOP algorithms and has about 20% improvement in solution quality.

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Section: Introductionmentioning

confidence: 99%

“…Unfortunately, C-CoCoA lacks the anytime attribute and has poor robustness on complex problems. Jeroen proposed the Distributed Bayesian (D-Bay) algorithm, which solves C-DCOP by utilizing Bayesian optimization for adaptive sampling of variables [11].…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Distribution Based Algorithm for Continuous Distributed Constraint Optimization Problems

Shi,

Zhang,

Liao

et al. 2024

ITC

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“…Therefore, Stranders et al [32] proposed the Continuous DCOP (C-DCOP) framework to extend DCOPs whose variables are continuous and the constraint utilities are functional forms. The C-DOCP algorithms include Continuous MS (CMS) [32], Hybrid CMS (HCMS) [33], Bayesian DPOP (B-DPOP) [34], Particle Swarm Based Continuous DCOP (PCD) [35], Exact Continuous DPOP (EC-DCOP) [36] and extensions (Approximate Continuous DPOP (AC-DPOP), Clustered AC-DPOP (CAC-DPOP), Continuous DSA (C-DSA)), Continuous Cooperative Constraint Approximation (C-CoCoA) [37], and Particle Swarm with Local Decision Based Continuous DCOP (PCD-LD) [38].…”

Section: Introductionmentioning

confidence: 99%

A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems

Liao,

Hoang

2024

Applied Sciences

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Distributed Constraint Optimization Problems (DCOPs) are an efficient framework widely used in multi-agent collaborative modeling. The traditional DCOP framework assumes that variables are discrete and constraint utilities are represented in tabular forms. However, the variables are continuous and constraint utilities are in functional forms in many practical applications. To overcome this limitation, researchers have proposed Continuous DCOPs (C-DCOPs), which can model DCOPs with continuous variables. However, most of the existing C-DCOP algorithms rely on gradient information for optimization, which means that they are unable to solve the situation where the utility function is a non-differentiable function. Although the Particle Swarm-Based C-DCOP (PCD) and Particle Swarm with Local Decision-Based C-DCOP (PCD-LD) algorithms can solve the situation with non-differentiable utility functions, they need to implement Breadth First Search (BFS) pseudo-trees for message passing. Unfortunately, employing the BFS pseudo-tree results in expensive computational overheads and agent privacy leakage, as messages are aggregated to the root node of the BFS pseudo-tree. Therefore, this paper aims to propose a fully distributed C-DCOP algorithm to solve the utility function form problem and avoid the disadvantages caused by the BFS pseudo-tree. Inspired by the population-based algorithms, we propose a fully decentralized local search algorithm, named Population-based Local Search Algorithm (PLSA), for solving C-DCOPs with three-fold advantages: (i) PLSA adopts a heuristic method to guide the local search to achieve a fast search for high-quality solutions; (ii) in contrast to the conventional C-DCOP algorithm, PLSA can solve utility functions of any form; and (iii) compared to PCD and PCD-LD, PLSA avoids complex message passing to achieve efficient computation and agent privacy protection. In addition, we implement an extended version of PLSA, named Population-based Global Search Algorithm (PGSA), and empirically show that our algorithms outperform the state-of-the-art C-DCOP algorithms on three types of benchmark problems.

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“…Therefore, Continuous DCOPs (C-DCOPs) [34] were proposed to model problems with continuous variables, and the constraint utilities are functional forms. Correspondingly, researchers proposed new C-DCOP algorithms to deal with the modification of the C-DCOP formulation, including Continuous Max-Sum (CMS) [34], Hybrid CMS (HCMS) [35], Bayesian Distributed Pseudo-tree Optimization Procedure (B-DPOP) [36], Particle Swarm Based Continuous DCOP Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.…”

Section: Introductionmentioning

confidence: 99%

A Class of Local Search Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

Liao,

Hoang

2024

Preprint

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Continuous Distributed Constraint Optimization Problem (C-DCOPs) are a powerful framework to model problems with continuous variables in multi-agent systems. Previous works about C-DCOP algorithms mainly use pseudo-trees to guarantee the anytime property or are without anytime property guarantees. However, there is a risk of privacy leakage in the pseudo-tree due to utility passing between agents. Therefore, based on the basic constraint graph instead of the pseudo-tree, we (i) extend the Maximum Gain Message (MGM) algorithm by combining the local search strategy to solve C-DCOPs, named Continuous MGM (C-MGM), and it’s able to guarantee the monotonicity of the solution quality; (ii) propose a Parallel C-MGM (C-PMGM) algorithm to improve the solution quality through parallel random search; and (iii) introduce the differential search into C-PMGM to design a Parallel Differential Search C-MGM (C-PDSM) algorithm, which constructs a heuristic method to speed up convergence and improve solution quality. Compared to other anytime C-DCOP algorithms using pseudo-trees, the proposed three algorithms can exhibit better performance in avoiding privacy violations. We theoretically prove that the proposed algorithms are the anytime algorithms and empirically demonstrate that our algorithms outperform the state-of-the-art C-DCOP algorithms.

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Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver

Cited by 5 publications

References 53 publications

An Estimation of Distribution Based Algorithm for Continuous Distributed Constraint Optimization Problems

An Estimation of Distribution Based Algorithm for Continuous Distributed Constraint Optimization Problems

A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems

A Class of Local Search Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

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