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Center for Nonlinear Studies |
Wide-range applications of cyber-physical systems(CPS) raise the issues of the resource allocation and load balancing frequently. A specialized, fast-convergence algorithm to solve those issues in real-time due to the time-constrained operational response is highly needed. Swarm intelligence based optimization algorithms simulate the cooperation and interaction behaviors from social and nature phenomena to solve complex, non-convex and/or ill-conditioned general nonlinear problems with high efficiency. Alternatively, multiagent coordination for networked systems has been extensively investigated in control theory, and multiagent consensus and synchronization are motivated by similar ideas from swarm intelligence. However, no research has yet to be done with regards to the relationship between these two research areas. Therefore, in this research, we bridge the gap between multiagent coordination and swarm intelligence optimization algorithms, and propose a novel multiagent coordination embedded swarm intelligence algorithm called “Multiagent Coordination Optimization”(MCO) to enhance the original algorithms when addressing the CPS problems. Moreover, the convergence issue for the proposed algorithm will be studied in a control theoretical perspective, and also in this talk, some numerical illustrations of the proposed algorithm to address those issues will be presented.
Host: Feng Pan