Publications

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.

@inproceedings{choudhury2020particle,
	title={A particle swarm based algorithm for functional distributed constraint optimization problems},
	author={Choudhury, Moumita and Mahmud, Saaduddin and Khan, Md Mosaddek},
	booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
	volume={34},
	number={05},
	pages={7111--7118},
	year={2020}
}
																

Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multiagent systems with a set of discrete variables. Later works have extended DCOPs to model problems with a set of continuous variables, named Functional DCOPs (F-DCOPs). In this paper, we combine both of these frameworks into the Mixed Integer Functional DCOP (MIF-DCOP) framework that can deal with problems regardless of their variables’ type. We then propose a novel algorithm − Distributed Parallel Simulated Annealing (DPSA), where agents cooperatively learn the optimal parameter configuration for the algorithm while also solving the given problem using the learned knowledge. Finally, we empirically evaluate our approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA produces solutions of significantly better quality than the state-of-the-art non-exact algorithms in their corresponding settings.

	 @inproceedings{saaduddinlearning,
	  title={Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs},
	  author={Saaduddin Mahmud, Md and Khan, Mosaddek and Choudhury, Moumita and Tran-Thanh, Long and Jennings, Nicholas R},
		booktitle={Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI)},
		pages={268–275},
		year={2020}
	}
																	

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.

	@inproceedings{mahmud2019aed,
	       title={AED: An Anytime Evolutionary DCOP},
	       author={Mahmud, Saaduddin and Choudhury, Moumita and Khan, Md and Tran-Thanh, Long and Jennings, Nicholas R and others},
		booktitle={Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
		pages={825–833},
		year={2020}
}
																	

Distributed Constraint Optimization Problems (DCOPs) have been widely used to coordinate interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model assumes that variables owned by the agents can take only discrete values and constraints' cost functions are defined for every possible value assignment of a set of variables. While this formulation is often reasonable, there are many applications where the variables are continuous decision variables and constraints are in functional form. To overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is able to model problems with continuous variables. The existing F-DCOPs algorithms experience huge computation and communication overhead. This paper applies continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. We empirically show that our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time compared to the existing F-DCOP algorithms.

	@article{sarker2020c,
	title={C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs},
	author={Sarker, Amit and Arif, Abdullahil Baki and Choudhury, Moumita and Khan, Md and others},
	journal={Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
	year={2020}
	}