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 assumed to be discrete. However, many applications, such as target tracking or sleep scheduling sensor networks, continuous-valued more suitable than discrete ones. To better model researchers have proposed Continuous DCOPs (C-DCOPs), an extension of that can explicitly problems with continuous variables. The state-of-the-art approaches solving C-DCOPs experience either onerous memory computation overhead and unsuitable non-differentiable optimization problems. address this issue, we propose new C-DCOP algorithm, namely Particle Swarm Based (PCD), which is inspired (PSO), well-known centralized population-based approach recent years, algorithms gained significant attention due their ability producing high-quality solutions. Nonetheless, the best our knowledge, class has not been utilized solve there no work evaluating potential PSO C-DCOPs. light observation, adapted PSO, decentralized manner. resulting PCD algorithm only produces good-quality solutions but also finds solution without any requirement derivative calculations. Moreover, design crossover operator used further improve quality found. Finally, theoretically prove anytime empirically evaluate against wide variety benchmarks.