Group Correction-based Local Disturbance Particle Swarm Optimization algorithm for solving Continuous Distributed Constraint Optimization Problems

Continuous Distributed Constraint Optimization Problems (C-DCOPs) are a significant constraint handling framework to model continuous variable problems of multi-agent systems. Many excellent algorithms have been designed to solve C-DCOPs in recent decades. However, these algorithms are prone to falling into local optimum, which is a major challenge in solving C-DCOPs. This paper proposes a Group Correction-based Local Disturbance Particle Swarm Optimization algorithm named GC-LDP to improve its solution quality. In GC-LDP, we introduce two items, the average of the personal best positions and the average of the personal current positions, into the velocity update formula of traditional Particle Swarm Optimization to utilize the group knowledge to correct the exploitation direction. In addition, a local disturbance strategy is designed in GC-LDP to increase the swarm diversity by searching the nearest particle group in the solution space to enhance the algorithm's exploration ability. GC-LDP has been theoretically proven to be an anytime algorithm. Furthermore, based on the extensive experiments on four types of benchmark problems, we demonstrate that GC-LDP outperforms state-of-the-art C-DCOP algorithms in terms of convergence speed and solution quality.