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AED: An Anytime Evolutionary DCOP Algorithm

AED: An Anytime Evolutionary DCOP Algorithm
AED: An Anytime Evolutionary DCOP Algorithm
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.
Distributed Problem Solving, DCOPs
2523-5699
825-833
International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Mahmud, Saaduddin
a211758a-bec8-4076-b5c5-e184a6f7d635
Choudhury, Moumita
6e77c82a-0bff-4529-8db3-014615256a88
Khan, Md. Mosaddek
6c5cfdba-17fd-4b64-9c26-97e562071ed2
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Jennings, Nicholas R.
3f6b53c2-4b6d-4b9d-bb51-774898f6f136
An, B
Yorke-Smith, N
Fallah Seghrouch, El
Sukthank, G
Mahmud, Saaduddin
a211758a-bec8-4076-b5c5-e184a6f7d635
Choudhury, Moumita
6e77c82a-0bff-4529-8db3-014615256a88
Khan, Md. Mosaddek
6c5cfdba-17fd-4b64-9c26-97e562071ed2
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Jennings, Nicholas R.
3f6b53c2-4b6d-4b9d-bb51-774898f6f136
An, B
Yorke-Smith, N
Fallah Seghrouch, El
Sukthank, G

Mahmud, Saaduddin, Choudhury, Moumita, Khan, Md. Mosaddek, Tran-Thanh, Long and Jennings, Nicholas R. (2020) AED: An Anytime Evolutionary DCOP Algorithm. An, B, Yorke-Smith, N, Fallah Seghrouch, El and Sukthank, G (eds.) In Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). pp. 825-833 .

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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More information

Published date: 13 May 2020
Venue - Dates: Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems, Auckland, New Zealand, Auckland, New Zealand, 2020-05-09 - 2020-05-13
Keywords: Distributed Problem Solving, DCOPs

Identifiers

Local EPrints ID: 468861
URI: http://eprints.soton.ac.uk/id/eprint/468861
ISSN: 2523-5699
PURE UUID: 31b67bbd-7831-4227-9cf4-2e11cde13a35
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

Catalogue record

Date deposited: 30 Aug 2022 16:45
Last modified: 19 Jul 2024 16:53

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Contributors

Author: Saaduddin Mahmud
Author: Moumita Choudhury
Author: Md. Mosaddek Khan
Author: Long Tran-Thanh ORCID iD
Author: Nicholas R. Jennings
Editor: B An
Editor: N Yorke-Smith
Editor: El Fallah Seghrouch
Editor: G Sukthank

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