Use Of Multi-objective Techniques Research Articles

Implications of parameter selection in dynamic multiobjective models in economics and finance

Time is a key variable in the field of economics and finance. However, most of the classic approaches to economic problems are static. In this pa- per, we first review the existing literature on the use of multiobjective techniques to control dynamic systems within the area of economics and finance. We also tackle the question of which measure should we use to evaluate alternative solutions. To this end, we elaborate on the meaning added by the selection of a parameter in a family of distance functions used to evaluate alternative solutions. Keywords: time, dynamic systems, economics, distance function, review.

Enhancing controller’s tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop

In general, the starting point for the complex task of designing a robust and efficient control system is the use of nominal models that allow to establish a first set of parameters for the selected control scheme. Once the initial stage of design is achieved, control engineers face the difficult task of Fine-Tuning for a more realistic environment, where the environment conditions are as similar as possible to the real system. For this reason, in the last decades the use of Hardware-in-The-Loop (HiL) systems has been introduced. This simulation technique guarantees realistic simulation environments to test the designs but without danger of damaging the equipment. Also, in this iterative process of Fine-Tuning, it is usual to use different (generally conflicting/opposed) criteria that take into account the sensitivities that always appear in every project, such as economic, security, robustness, performance, for example. In this framework, the use of multi-objective techniques are especially useful since they allow to study the different design alternatives based on the multiple existing criteria. Unfortunately, the combination of multi-objective techniques and verification schemes based on Hardware-In-The-Loop presents a high incompatibility. Since obtaining the optimal set of solutions requires a high computational cost that is greatly increased when using Hardware- In-the-Loop. For this reason, it is often necessary to use less realistic but more computationally efficient verification schemes such as Model in the Loop (MiL), Software in the Loop (SiL) and Processor in the Loop (PiL). In this paper, a combined methodology is presented, where multi-objective optimisation and multi-criteria decision making steps are sequentially performed to achieve a final control solution. The authors claim that while going towards the optimisation sequence over MiL → SiL → PiL → HiL platforms, the complexity of the problem is unveiled to the designer, allowing to state meaningful design objectives. In addition, safety in the step between simulation and reality is significantly increased.