Here we present a detailed ab initio study of two experimentally synthesized bismuth niobate BiNbO4 (BNO) polymorphs within the framework of density functional theory (DFT).

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.

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.

This paper develops a new approach to speed up Generalized Distributive Law (GDL) based message passing algorithms that are used to solve large-scale Distributed Constraint Optimization Problems (DCOPs) in multi-agent systems. In particular, we significantly reduce computation and communication costs in terms of convergence time for algorithms such as Max-Sum, Bounded Max-Sum, Fast Max-Sum, Bounded Fast Max-Sum, BnB Max-Sum, BnB Fast Max-Sum and Generalized Fast Belief Propagation. This is important since it is often observed that the outcome obtained from such algorithms becomes outdated or unusable if the optimization process takes too much time. Specifically, the issue of taking too long to complete the internal operation of a DCOP algorithm is even more severe and commonplace in a system where the algorithm has to deal with a large number of agents, tasks and resources. This, in turn, limits the practical scalability of such algorithms. In other words, an optimization algorithm can be used in larger systems if the completion time can be reduced. However, it is challenging to maintain the solution quality while minimizing the completion time. Considering this trade-off, we propose a generic message passing protocol for GDL-based algorithms that combines clustering with domain pruning, as well as the use of a regression method to determine the appropriate number of clusters for a given scenario. We empirically evaluate the performance of our method in a number of settings and find that it brings down the completion time by around 37-85% (1.6-6.5 times faster) for 100-900 nodes, and by around 47-91% (1.9-11 times faster) for 3000-10 000 nodes compared to the current state-of-the-art.

A Multi-Agent Path Finding (MAPF) problem involves multiple agents who want to reach their destinations without obstructing other agents. Although a MAPF problem needs to be solved for many real-world deployments, solving such a problem optimally is NP-hard. Many approaches have been proposed in the literature that offers sub-optimal solutions to this problem. For example, the Enhanced Conflict Based Search (ECBS) algorithm compromises the solution quality up to a constant factor to gain a notable runtime improvement. However, these algorithms use a fixed global sub-optimal bound for all agents, regardless of their preferences. In effect, with the increase in the number of agents, the runtime performance degrades. Against this backdrop, with the intent to further speed up the runtime, we propose an adaptive agent-specific sub-optimal bounding approach, called ASB-ECBS, that can be executed statically or dynamically. Specifically, ASB-ECBS can assign sub-optimal bound considering an individual agent's requirement. Additionally, we theoretically prove that the solution cost of ASB-ECBS remains within the sub-optimal bound. Finally, our extensive empirical results depict a notable improvement in the runtime by using ASB-ECBS while reducing the search space compared to the state-of-the-art MAPF algorithms.