A particle swarm inspired approach for continuous distributed constraint optimization problems
Distributed Constraint Optimization Problems (DCOPs) are a widely studied framework for coordinating interactions in cooperative multi-agent systems. In classical DCOPs, variables owned by agents are assumed to be discrete. However, in many applications, such as target tracking or sleep scheduling i...
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| Vydané v: | Engineering applications of artificial intelligence Ročník 123; s. 106280 |
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| Jazyk: | English |
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Elsevier Ltd
01.08.2023
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| ISSN: | 0952-1976 |
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| Abstract | Distributed Constraint Optimization Problems (DCOPs) are a widely studied framework for coordinating interactions in cooperative multi-agent systems. In classical DCOPs, variables owned by agents are assumed to be discrete. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous-valued variables are more suitable than discrete ones. To better model such applications, researchers have proposed Continuous DCOPs (C-DCOPs), an extension of DCOPs, that can explicitly model problems with continuous variables. The state-of-the-art approaches for solving C-DCOPs experience either onerous memory or computation overhead and are unsuitable for non-differentiable optimization problems. To address this issue, we propose a new C-DCOP algorithm, namely Particle Swarm Optimization Based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO), a well-known centralized population-based approach for solving continuous optimization problems. In recent years, population-based algorithms have gained significant attention in classical DCOPs due to their ability in producing high-quality solutions. Nonetheless, to the best of our knowledge, this class of algorithms has not been utilized to solve C-DCOPs and there has been no work evaluating the potential of PSO in solving classical DCOPs or C-DCOPs. In light of this observation, we adapted PSO, a centralized algorithm, to solve C-DCOPs in a decentralized manner. The resulting PCD algorithm not only produces good-quality solutions but also finds solution without any requirement for derivative calculations. Moreover, we design a crossover operator that can be used by PCD to further improve the quality of solutions found. Finally, we theoretically prove that PCD is an anytime algorithm and empirically evaluate PCD against the state-of-the-art C-DCOP algorithms in a wide variety of benchmarks. |
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| AbstractList | Distributed Constraint Optimization Problems (DCOPs) are a widely studied framework for coordinating interactions in cooperative multi-agent systems. In classical DCOPs, variables owned by agents are assumed to be discrete. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous-valued variables are more suitable than discrete ones. To better model such applications, researchers have proposed Continuous DCOPs (C-DCOPs), an extension of DCOPs, that can explicitly model problems with continuous variables. The state-of-the-art approaches for solving C-DCOPs experience either onerous memory or computation overhead and are unsuitable for non-differentiable optimization problems. To address this issue, we propose a new C-DCOP algorithm, namely Particle Swarm Optimization Based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO), a well-known centralized population-based approach for solving continuous optimization problems. In recent years, population-based algorithms have gained significant attention in classical DCOPs due to their ability in producing high-quality solutions. Nonetheless, to the best of our knowledge, this class of algorithms has not been utilized to solve C-DCOPs and there has been no work evaluating the potential of PSO in solving classical DCOPs or C-DCOPs. In light of this observation, we adapted PSO, a centralized algorithm, to solve C-DCOPs in a decentralized manner. The resulting PCD algorithm not only produces good-quality solutions but also finds solution without any requirement for derivative calculations. Moreover, we design a crossover operator that can be used by PCD to further improve the quality of solutions found. Finally, we theoretically prove that PCD is an anytime algorithm and empirically evaluate PCD against the state-of-the-art C-DCOP algorithms in a wide variety of benchmarks. |
| ArticleNumber | 106280 |
| Author | Khan, Md. Maruf Al Alif Yeoh, William Choudhury, Moumita Yaser, Samin Sarker, Amit Khan, Md. Mosaddek |
| Author_xml | – sequence: 1 givenname: Moumita surname: Choudhury fullname: Choudhury, Moumita email: amchoudhury@cs.umass.edu organization: College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, USA – sequence: 2 givenname: Amit orcidid: 0000-0001-7883-6594 surname: Sarker fullname: Sarker, Amit email: asarker@cics.umass.edu organization: College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, USA – sequence: 3 givenname: Samin surname: Yaser fullname: Yaser, Samin email: samin-2017014979@cs.du.ac.bd organization: Department of Computer Science and Engineering, University of Dhaka, Science Complex, Dhaka 1000, Bangladesh – sequence: 4 givenname: Md. Maruf Al Alif surname: Khan fullname: Khan, Md. Maruf Al Alif email: alif-2017014997@cs.du.ac.bd organization: Department of Computer Science and Engineering, University of Dhaka, Science Complex, Dhaka 1000, Bangladesh – sequence: 5 givenname: William surname: Yeoh fullname: Yeoh, William email: wyeoh@wustl.edu organization: Department of Computer Science and Engineering, Washington University in St. Louis, One Brookings Dr., St. Louis, MO 63130, USA – sequence: 6 givenname: Md. Mosaddek orcidid: 0000-0002-7871-7111 surname: Khan fullname: Khan, Md. Mosaddek email: mosaddek@du.ac.bd organization: Department of Computer Science and Engineering, University of Dhaka, Science Complex, Dhaka 1000, Bangladesh |
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