Distributed Multi-objective Multidisciplinary Design Optimization Algorithms

TL;DR

This paper introduces a multi-agent system approach for distributed multi-objective multidisciplinary design optimization, utilizing graph theory and local consensus algorithms to achieve globally optimal, consistent designs with proven convergence.

Contribution

It presents a novel distributed algorithm framework that combines graph theoretic constructs with consensus methods for efficient multidisciplinary design optimization.

Findings

Algorithms converge to locally and globally optimal designs.

Effective in achieving consistent shared variables across subspaces.

Simulations demonstrate the framework's effectiveness.

Abstract

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.