Group Decision Makers' Preferences Modelling within the Goal Programming Model: An Overview and a Typology

Expert Systems with Applications

et al. 2016

Highlights• We present an evolutionary multiobjective optimisation approach for PI controller tuning.• This approach incorporates designer´s preferences into the optimisation process.• The methodology is evaluated in a multivariable process.• It is possible to improve pertinency of the approximated Pareto front. AbstractMulti-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applications. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorporated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark example in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.

Section: Background On Preference Handling In Multi-objective Optimismentioning

confidence: 99%

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Expert Systems with Applications

et al. 2016

“…That is, the capacity to obtain a set of interesting solutions from the DM point of view (Figure 4c). The designer's preferences could be defined in the MOOD procedure in an a priori, progressive, or a posteriori fashion [35]. A straightforward alternative to improve pertinency of solutions could be done by means of optimisation constraints (besides bound constraints on decision variables).…”

Section: Pertinencymentioning

confidence: 99%

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Control Engineering Practice

et al. 2014

118

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach to calculate a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective searching for a set of potentially preferable solutions; the designer may then analyse the trade-off among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are commented on. Through this paper it is noticed that EMO research has been developing towards different optimization statements, but such statements are not commonly used on controller tuning. Therefore gaps between EMO research and EMO applications on controller tuning are detected and suggested as potential trends for research.

“…The designer's preferences could be defined in the MOOD procedure in an a priori, progressive, or a posteriori fashion [38].…”

Section: Background On Preference Handling In Multi-objective Optimismentioning

confidence: 99%

Physical programming for preference driven evolutionary multi-objective optimization

et al. 2014

Applied Soft Computing

Preference articulation in multi-objective optimisation could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimisation process towards the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimisation is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimisation.