Distributed Constraint Optimization Research Articles

DCSACA: distributed constraint service-aware collaborative access algorithm based on large-scale access to the Internet of Things

With the rapid development of the smart city and the Internet Plus-themed multi-network applications, it is becoming increasingly difficult for the data access center for the Internet of Things (DACIOT) to meet large-scale users’ service requirements with low latency and high quality while sending service access requests. This paper first converts the problem of a large number of access requests to DACIOT into a distributed constraint optimization problem. Then, in order to address the optimization problem, a dynamic multi-constraint service-aware collaborative access algorithm is proposed based on dynamic load feedback from the access nodes, which can effectively reduce network congestion through load feedback and improve access performance. The algorithm firstly defines the dynamic context load sensing model, which is able to detect the load metrics of access clusters and assist access servers to work together to improve the availability of DACIOT, then it uses a heuristic falling search algorithm to search for the optimal resource on the basis of this model, after which it analyzes the convergence of the access algorithm. Experimental results show that the algorithm can effectively improve the rate of success, lower the network delay of access requests and reduce network jitter when accessing DACIOT.

Distributed Constraint Optimization with Flocking Behavior

This paper studies distributed optimization having flocking behavior and local constraint set. Multiagent systems with continuous‐time and second‐order dynamics are studied. Each agent has a local constraint set and a local objective function, which are known to only one agent. The objective is for multiple agents to optimize a sum of the local functions with local interaction and information. First, a bounded potential function to construct the controller is given and a distributed optimization algorithm that makes a group of agents avoid collisions during the evolution is presented. Then, it is proved that all agents track the optimal velocity while avoiding collisions. The proof of the main result is divided into three steps: global set convergence, consensus analysis, and optimal set convergence. Finally, a simulation is included to illustrate the results.

Dual-decomposition-based peer-to-peer voltage control for distribution networks

The increasing penetration of distributed energy resources (DERs) creates new voltage issues in distribution networks. This study proposes an algorithm that mitigates these issues, by actively managing the active and reactive power of those DERs. The control problem is formulated as an optimisation problem. The study proposes solving the problem in a fully distributed manner and presents a methodology to convert a centralised constraint optimisation problem into a fully distributed constraint optimisation problem based on dual decomposition, linearised model of the distribution network and peer-to-peer communication protocol. A real low voltage residential semi-urban feeder from the region of Flanders, Belgium has been used as a case study. The simulation results show the ability of the proposed peer-to-peer control algorithm to control the voltage effectively within limits.

Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids

In this paper, an optimization problem is formulated for the automatic back-feed service restoration in smart distribution grids. The formulated problem relies on the structure of smart distribution grids, clustered into multi-microgrids, capable of operating in both grid-connected and islanded modes of operation. To that end, three types of power transfer between the neighboring microgrids, during the restoration processes are introduced: load transfer, distributed generation (DG) transfer, and combined load–DG transfer. The formulated optimization problem takes into account the ability of forming new, not predefined islanded microgrids, in the post-restoration configuration, to maximize service restoration. To obviate the need for a central unit, the optimization problem is reformulated, in this work, asa distributed constraint optimization problem, in which the variables and constraints are distributed among automated agents. To reduce the problem complexity, the restoration problem is decomposed into two sequential and interdependent distributed sub-problems: supply adequacy, and optimal reconfiguration. The proposed algorithm adopts the Optimal Asynchronous Partial Overlay (OPTAPO) technique, which is based on the distributed constraint agent search to solve distributed sub-problems in a multi-agent environment. Several case studies have been carried out to evaluate the effectiveness and robustness of the proposed algorithm.

Applying Distributed Constraint Optimization Approach to the User Association Problem in Heterogeneous Networks.

User association has emerged as a distributed resource allocation problem in the heterogeneous networks (HetNets). Although an approximate solution is obtainable using the approaches like combinatorial optimization and game theory-based schemes, these techniques can be easily trapped in local optima. Furthermore, the lack of exploring the relation between the quality of the solution and the parameters in the HetNet [e.g., the number of users and base stations (BSs)], at what levels, impairs the practicability of deploying these approaches in a real world environment. To address these issues, this paper investigates how to model the problem as a distributed constraint optimization problem (DCOP) from the point of the view of the multiagent system. More specifically, we develop two models named each connection as variable (ECAV) and each BS and user as variable (EBUAV). Hereinafter, we propose a DCOP solver which not only sets up the model in a distributed way but also enables us to efficiently obtain the solution by means of a complete DCOP algorithm based on distributed message-passing. Naturally, both theoretical analysis and simulation show that different qualitative solutions can be obtained in terms of an introduced parameter which has a close relation with the parameters in the HetNet. It is also apparent that there is 6% improvement on the throughput by the DCOP solver comparing with other counterparts when . Particularly, it demonstrates up to 18% increase in the ability to make BSs service more users when the number of users is above 200 while the available resource blocks (RBs) are limited. In addition, it appears that the distribution of RBs allocated to users by BSs is better with the variation of the volume of RBs at the macro BS.

Forward bounding on pseudo-trees for DCOPs and ADCOPs

Complete search algorithms for solving Distributed constraint optimization problems (DCOPs) can be divided into two groups: algorithms that use a pseudo tree and algorithms that do not use one. The best performing algorithms that do not use a pseudo tree use some form of forward bounding. In order to try and gain from both worlds, a new algorithm, which incorporates a hybrid approach of the two groups is presented. The proposed algorithm – Pseudo-Tree Forward-bounding (PT-FB) – is shown to perform very well. PT-FB is next extended to be able to solve Asymmetric DCOPs (ADCOPs). Here again, its performance is better than its competitors. An extensive experimental evaluation of the performance of each of the proposed algorithms is presented.

DUCT

We propose a distributed upper confidence bound approach, DUCT, for solving distributed constraint optimization problems. We compare four variants of this approach with a baseline random sampling algorithm, as well as other complete and incomplete algorithms for DCOPs. Under general assumptions, we theoretically show that the solution found by DUCT after T steps is approximately T −1 -close to the optimal. Experimentally, we show that DUCT matches the optimal solution found by the well-known DPOP and O-DPOP algorithms on moderate-size problems, while always requiring less agent communication. For larger problems, where DPOP fails, we show that DUCT produces significantly better solutions than local, incomplete algorithms. Overall, we believe that DUCT is a practical, scalable algorithm for complex DCOPs.

Solving distributed constraint optimization problems using logic programming

AbstractThis paper explores the use ofAnswer Set Programming (ASP)in solvingDistributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) it shows how one can formulate DCOPs as logic programs; (2) it introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) it experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) it demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs.

Agent-Based Microgrid Scheduling: An ICT Perspective

New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of the operation of distributed generators (DG) and renewable energy resources (RES) in microgrids is a problem that requires tough planning and the use of advanced tools to be efficiently addressed. This paper discusses different ICT technologies that can enable microgrid communication for control and management of distributed energy resources (DER). Based on such ICT, we propose a novel agent-based model to address the long-term scheduling of DER in microgrids as a distributed constraint optimization problem (DCOP). However, finding the optimal solution for a DCOP is known to be an NP-Hard problem, making difficult to guarantee optimal solutions even for short optimization periods. Hence, for the long-term scheduling of DER, we propose to split the problem into small time windows that can be effectively solved sequentially by off-the-shelf DCOP algorithms. A particular, but general enough case study is used to compare different DCOP algorithms under the proposed model. Results show that DCOP algorithms can find optimal and near-optimal solutions depending on the window size and the scenario considered.

Decentralized Power Distribution in the Smart Grid with Ancillary Lines

Energy management is a key topic for today’s society, and a crucial challenge is the shift from a production system based on fossil fuel to sustainable energy. A key ingredient for this important step is the use of a highly automated power delivery network, where intelligent devices can communicate and collaborate to optimize energy management. This paper investigates a specific model for smart power grids initially proposed by Zdeborov et al. (Phys Rev E Stat Nonlinear Soft Matter Phys 80(4): 2009) where backup power lines connect a subset of loads to generators so to meet the demand of the whole network. Specifically, we extend such model to minimize CO2 emissions related to energy production. In more detail, we propose a formalization for this problem based on the Distributed Constraint Optimization Problem (DCOP) framework and a solution approach based on the min-sum algorithm. We empirically evaluate our approach on a set of benchmarking power grid instances comparing our proposed solution to simulated annealing and to the DSA algorithm. Our results show that min-sum favorably compares with simulated annealing and DSA providing a promising solution method for this model.

Node and Link Allocation in Network Virtualization Based on Distributed Constraint Optimization

Virtual Networks (VNs) offer a flexible and economic approach to deploy customer suited networks. However, defining how resources of a physical network are used to support VNs requirements is a NP-hard problem. For this reason, heuristics have been used on mapping of virtual networks. Although heuristics do not ensure the optimal solution, they implement fast solutions and showed satisfactory results. This work presents a modeling of the node and link allocation problem using Distributed Constraint Optimization Problem (DCOP) with factor graphs, which is a formalism widely used in real distributed optimization problems. In our approach, we use the max-sum algorithm to solve the DCOP. Correctness criteria for this approach are discussed and verifications are conducted through model checking.

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

Depth First Search (DFS) pseudo-tree is popularly used as the communication structure in complete algorithms for solving Distributed Constraint Optimization Problems (DCOPs) from multiagent systems. The advantage of a DFS pseudo-tree lies in its parallelism derived from pseudo-tree branches because the nodes in different branches are relatively independent and can compute concurrently. However, the constructed DFS pseudo-trees in experiments often come to be chain-like and greatly impair the performances of solving algorithms. Therefore, we propose a new DPOP algorithm using a Breadth First Search (BFS) pseudo-tree as the communication structure, named BFSDPOP. Compared with a DFS pseudo-tree, a BFS pseudo-tree is more excellent on the parallelism as it has much more branches. Another notable advantage is that the height of a BFS pseudo-tree is much lower than that of a DFS pseudo-tree, which gives rise to the shorter communication paths and less communication time. The method of Cluster Removing is also presented to allocate cross-edge constraints to reduce the size of the largest message in BFSDPOP. In the experiment, BFSDPOP with a BFS pseudo-tree and original DPOP with a DFS pseudo-tree are compared on three types of problems - graph coloring problems, meeting scheduling problems and random DCOPs. The results show that BFSDPOP outperforms original DPOP in most cases, which proves the excellent attributes of BFS pseudo-tree over DFS pseudo-tree.

Layering-ADOPT: ADOPT with Layering Boundary

ADOPT and BnB-ADOPT are the two important search-based complete algorithms to solve Distributed Constraint Optimization Problem (DCOP). However, a large number of solution reconstructions appear in ADOPT and sub-optimal branch can’t be promptly pruned in BnB-ADOPT. A layering DCOP algorithm to combine ADOPT and BnB-ADOPT is proposed to remedy their weakness, named Layering-ADOPT. In Layering- ADOPT, a layering boundary is introduced to divide all agents into two classes, one performing ADOPT and another for BnB-ADOPT. This paper presents a rule to get a layering boundary and a new strategy to realize the joint between ADOPT and BnB-ADOPT. In the experiment, Layering-ADOPT and ADOPT as well as BnB-ADOPT are compared on random DCOPs. The results show that Layering-ADOPT is superior to ADOPT and BnB-ADOPT on the test problems.

Balancing exploration and exploitation in incomplete Min/Max-sum inference for distributed constraint optimization

Distributed Constraint Optimization Problems (DCOPs) are NP-hard and therefore the number of studies that consider incomplete algorithms for solving them is growing. Specifically, the Max-sum algorithm has drawn attention in recent years and has been applied to a number of realistic applications. Unfortunately, in many cases Max-sum does not produce high-quality solutions. More specifically, Max-sum does not converge and explores solutions of low quality when run on problems whose constraint graph representation contains multiple cycles of different sizes. In this paper we advance the state-of-the-art in incomplete algorithms for DCOPs by: (1) proposing a version of the Max-sum algorithm that operates on an alternating directed acyclic graph (Max-sum_AD), which guarantees convergence in linear time; (2) solving a major weakness of Max-sum and Max-sum_AD that causes inconsistent costs/utilities to be propagated and affect the assignment selection, by introducing value propagation to Max-sum_AD (Max-sum_ADVP); and (3) proposing exploration heuristic methods that evidently improve the algorithms performance further. We prove that Max-sum_ADVP converges to monotonically improving states after each change of direction, and that it is guaranteed to converge in pseudo-polynomial time to a stable solution that does not change with further changes of direction. Our empirical study reveals a large improvement in the quality of the solutions produced by Max-sum_ADVP on various benchmarks, compared to the solutions produced by the standard Max-sum algorithm, Bounded Max-sum and Max-sum_AD with no value propagation. It is found to be the best guaranteed convergence inference algorithm for DCOPs. The exploration methods we propose for Max-sum_ADVP improve its performance further. However, anytime results demonstrate that their exploration level is not as efficient as a version of Max-sum, which uses Damping.

CoCoA: A Non-Iterative Approach to a Local Search (A)DCOP Solver

We propose a novel incomplete cooperative algorithm for distributed constraint optimization problems (DCOPs) denoted as Cooperative Constraint Approximation (CoCoA). The key strategy of the algorithm is to use a semi-greedy approach in which knowledge is distributed amongst neighboring agents, and assigning a value only once instead of an iterative approach. Furthermore, CoCoA uses a unique-first approach to improve the solution quality. It is designed such that it can solve DCOPs as well as Asymmetric DCOPS, with only few messages being communicated between neighboring agents. Experimentally, through evaluating graph coloring problems, randomized (A)DCOPs, and a sensor network communication problem, we show that CoCoA is able to very quickly find solutions of high quality with a smaller communication overhead than state-of-the-art DCOP solvers such as DSA, MGM-2, ACLS, MCS-MGM and Max-Sum. In our asymmetric use case problem of a sensor network, we show that CoCoA not only finds the best solution, but also finds this solution faster than any other algorithm.

Preference Elicitation in DCOPs for Scheduling Devices in Smart Buildings

Researchers have used Distributed Constraint Optimization Problems (DCOPs) as a powerful approach to model various multi-agent coordination problems, taking into account their preferences and constraints. A core limitation of this model is the assumption that all agents’ preferences are specified a priori. However, in a number of application domains such knowledge become available only after being elicited from users in these domains. In this abstract, we explore the effects of preference elicitation in our motivating application of scheduling smart appliances with the aim of reducing users’ electricity bill cost as well as increasing their comfort.

Leximin Asymmetric Multiple Objective Distributed Constraint Optimization Problem

The Distributed Constraint Optimization Problem (DCOP) lies at the foundations of multiagent cooperation. With DCOPs, the optimization in distributed resource allocation problems is formalized using constraint optimization problems. The solvers for the problem are designed based on decentralized cooperative algorithms that are performed by multiple agents. In a conventional DCOP, a single objective is considered.The Multiple Objective Distributed Constraint Optimization Problem (MODCOP) is an extension of the DCOP framework, where agents cooperatively have to optimize simultaneously multiple objective functions. In the conventional MODCOPs, a few objectives are globally defined and agents cooperate to find the Pareto optimal solution. However, such models do not capture the interests of each agent. On the other hand, in several practical problems, the share of each agent is important. Such shares are modeled as preference values of agents. This class of problems can be defined using the MODCOP on the preferences of agents. In particular, we define optimization problems based on leximin ordering and Asymmetric DCOPs (Leximin AMODCOPs). The leximin defines an ordering among vectors of objective values. In addition, Asymmetric DCOPs capture the preferences of agents. Because the optimization based on the leximin ordering improves the equality among the satisfied preferences of the agents, this class of problems is important. We propose several solution methods for Leximin AMODCOPs generalizing traditional operators into the operators on sorted objective vectors and leximin. The solution methods applied to the Leximin AMODCOPs are based on pseudo trees. Also, the investigated search methods employ the concept of boundaries of the sorted vectors.

BD-ADOPT: a hybrid DCOP algorithm with best-first and depth-first search strategies

Distributed Constraint Optimization Problem (DCOP) is a promising framework for modeling a wide variety of multi-agent coordination problems. Best-First search (BFS) and Depth-First search (DFS) are two main search strategies used for search-based complete DCOP algorithms. Unfortunately, BFS often has to deal with a large number of solution reconstructions whereas DFS is unable to promptly prune sub-optimal branch. However, their weaknesses will be remedied if the two search strategies are combined based on agents’ positions in a pseudo-tree. Therefore, a hybrid DCOP algorithm with the combination of BFS and DFS, called BD-ADOPT, is proposed, in which a layering boundary is introduced to divide all agents into BFS-based agents and DFS-based agents. Furthermore, this paper gives a rule to find a suitable layering boundary with a new strategy for the agents near the boundary to realize the seamless joint between BFS and DFS strategies. Detailed experimental results show that BD-ADOPT outperforms some famous search-based complete DCOP algorithms on the benchmark problems.

Multi-Target Engagement in Complex Mobile Surveillance Sensor Networks

Efficient use of the network’s resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network’s success in tracking targets, efficiently and effectively, hinges significantly on the network’s ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation; (ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multi-sensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.

P-SyncBB: A Privacy Preserving Branch and Bound DCOP Algorithm

Distributed constraint optimization problems enable the representation of many combinatorial problems that are distributed by nature. An important motivation for such problems is to preserve the privacy of the participating agents during the solving process. The present paper introduces a novel privacy-preserving branch and bound algorithm for this purpose. The proposed algorithm, P-SyncBB, preserves constraint, topology and decision privacy. The algorithm requires secure solutions to several multi-party computation problems. Consequently, appropriate novel secure protocols are devised and analyzed. An extensive experimental evaluation on different benchmarks, problem sizes, and constraint densities shows that P-SyncBB exhibits superior performance to other privacy-preserving complete DCOP algorithms.