Can I read A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems on EtoBox?

A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems by Moumita Choudhury; Amit Sarker; Samin Yaser; Md. Maruf Al Alif Khan; William Yeoh; Md. Mosaddek Khan is a Computer Science article available to read on EtoBox.

What is A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems about?

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, th

Who reads A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems?

Author

Moumita Choudhury; Amit Sarker; Samin Yaser; Md. Maruf Al Alif Khan; William Yeoh; Md. Mosaddek Khan

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)