Distributed Multi-objective Multidisciplinary Design Optimization Algorithms
For engineers in multidisciplinary design optimization, this provides a distributed framework that coordinates local subspaces to achieve global optimality, though it is an incremental extension of existing consensus-based methods.
This work proposes a multi-agent system for distributed multi-objective multidisciplinary design optimization, where agents exchange and update shared design variables locally. The algorithm achieves consensus and asymptotic convergence to optimal and consistent designs, with simulations demonstrating its effectiveness.
This work proposes multi-agent systems setting for concurrent engineering system design optimization and gradually paves the way towards examining graph theoretic constructs in the context of multidisciplinary design optimization problem. The flow of the algorithm can be described as follow; generated estimates of the optimal (shared design) variables are exchanged locally with neighbor subspaces and then updated by computing a weighted sum of the local and received estimates. To comply with the consistency requirement, the resultant values are projected to local constraint sets. By employing the existing rules and results of the field, it has shown that the dual task of reaching consensus and asymptotic convergence of the algorithms to locally and globally optimal and consistent designs can be achieved. Finally, simulations are provided to illustrate the effectiveness and capability of the presented framework.