1. Multi-Objective Collaborative Optimization Based on Evolutionary Algorithms
- Author
-
Fan Zijie, Su Ruiyi, and Gui Liangjin
- Subjects
Computer science ,business.industry ,Mechanical Engineering ,Evolutionary algorithm ,Complex system ,Shared variables ,Computer Graphics and Computer-Aided Design ,Computer Science Applications ,Mechanics of Materials ,Collaborative optimization ,Artificial intelligence ,business ,Discipline ,Independence (probability theory) - Abstract
This paper proposes a novel multi-objective collaborative optimization (MOCO) approach based on multi-objective evolutionary algorithms for complex systems with multiple disciplines and objectives, especially for those systems in which most of the disciplinary variables are shared. The shared variables will conflict when the disciplinary optimizers are implemented concurrently. In order to avoid the confliction, the shared variables are treated as fixed parameters at the discipline level in most of the MOCO approaches. But in this paper, a coordinator is introduced to handle the confliction, which allocates more design freedom and independence to the disciplinary optimizers. A numerical example is solved, and the results are discussed.
- Published
- 2011