Abstract
Many-objective optimization problem (MaOP) is defined as optimization with more than 3 objective functions. This high number of objectives makes the comparing solutions more challenging. This holds true for design problems which are MaOPs by nature due to the inherent complexity and multifaceted nature of real-life applications. In the last decades, many strategies have attempted to overcome MaOPs such as removing objectives based on their impact on the optimization. However, from a design perspective, removing objectives could lead to an under optimal, unfeasible or unreliable design. Consequently, objective aggregation seems to be a better approach since objectives can be grouped based on design features controlled by the designers. The proposed methodology uses Axiomatic Design to decompose a system into subsystems or components, and Product-Related Dependencies Management to identify the dependencies between components and formulate the objectives. Then, these objectives are aggregated based on the subsystems found with the Axiomatic Design. The methodology, applied to the layout synthesis of an autonomous greenhouse, can trim down the number of objectives from 15 to 5. Then, using a modified non-dominated sorting genetic algorithm-II (NSGA-II) combined with the objective aggregation, we were able to increase the number of “good” concepts found from 9 to 33 out of a total of 50 obtained designs.
Optimal layout synthesis for quantum computing