Multi-objective worst case optimization by means of evolutionary algorithms

Many real-world optimization problems are subject to uncertainty. A possible goal is then to find a solution which is robust in the sense that it has the best worst-case performance over all possible scenarios. However, if the problem also involves mul- tiple objectives, which scenario is “best” or “worst” depends on the user’s weighting of the different criteria, which is generally difficult to specify before alternatives are known. Evolutionary multi-objective optimization avoids this problem by searching for the whole front of Pareto optimal solutions. This paper extends the concept of Pareto dominance to worst case optimization problems and demonstrates how evolu- tionary algorithms can be used for worst case optimization in a multi-objective setting

Gearbox design for uncertain load requirements using active robust optimization

Design and optimization of gear transmissions have been intensively studied, but surprisingly the robustness of the resulting optimal design to uncertain loads has never been considered. Active Robust (AR) optimization is a methodology to design products that attain robustness to uncertain or changing environmental conditions through adaptation. In this study the AR methodology is utilized to optimize the number of transmissions, as well as their gearing ratios, for an uncertain load demand. The problem is formulated as a bi-objective optimization problem where the objectives are to satisfy the load demand in the most energy efficient manner and to minimize production cost. The results show that this approach can find a set of robust designs, revealing a trade-off between energy efficiency and production cost. This can serve as a useful decision-making tool for the gearbox design process, as well as for other applications

A computational study on airflow balancing in a horticultural drying chamber

Simulations were conducted to study the airflow across skids of grapes in a horticultural grape drying chamber for the purpose of balancing the airflow to produce a uniform drying environment. The focus of the study was on the approach taken to provide balanced airflow using a computational fluid dynamics (CFD) tool combined with experimental data. The process was to first characterize the crate stacks by comparison of airflow simulations across a single crate stack to experimental data to establish resistance coefficients. The next step was to use these coefficients to simulate a row of stacked skids to establish corrections in terms of additional (variable) resistance that would result in balanced airflow. The corrected model was then used to simulate flow through the entire horticultural chamber to confirm that under the conditions of fan operation, the balance of airflow persists. The study shows that while the unmodified stacks had nearly 20% imbalance from the first to the last stack, the stack with resistance modifiers corrected this imbalance to within 5%, which is considered suitable for operation of the chamber.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author