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

Ribeiro

Carreño-Alvarado³

2017

Water

Self Cite

Abstract:Dealing with real world engineering problems, often comes with facing multiple and conflicting objectives and requirements. Water distributions systems (WDS) are not exempt from this: while cost and hydraulic performance are usually conflicting objectives, several requirements related with environmental issues in water sources might be in conflict as well. Commonly, optimisation statements are defined in order to address the WDS design, management and/or control. Multi-objective optimisation can handle such conflicting objectives, by means of a simultaneous optimisation of the design objectives, in order to approximate the so-called Pareto front. In such algorithms it is possible to embed preference handling mechanisms, with the aim of improving the pertinency of the approximation. In this paper we propose two mechanisms to handle such preferences based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and PROMETHEE (Preference Ranking Organisation METHod for Enrichment of Evaluations) methods. Performance evaluation on two benchmarks validates the usefulness of such approaches according to the degree of flexibility to capture designers' preferences.

“…In the case of the MOO process, special (or particular) circumstances might require additional mechanisms to deal successfully with a given MOP [40,42]. Some of them are listed below:…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Comparison of Preference Handling Techniques in Multi-Objective Optimisation for Water Distribution Systems

Ribeiro

Carreño-Alvarado³

2017

Water

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“…Therefore, a Multiobjective Optimisation Design (MOOD) procedure for controller tuning is needed, where the multi-objective problem (MOP) definition, the optimisation process and the MCDM stage are integrated. This procedure has shown to be a valuable tool for control engineers (Reynoso-Meza et al, 2013b, 2014c; it may enable the designer to have a close embedment with the tuning process; with them it is possible to take into account each design objective individually; it also enables the design alternative comparison, to select a controller fulfilling the expected trade-off among conflictive objectives.…”

Section: Introductionmentioning

confidence: 98%

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

Expert Systems with Applications

Sanchis

Blasco

et al. 2016

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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.

“…In the case of multi-objective optimisation, several algorithms have been designed (NBI [5], NNC 1 [6,7], NSGA-II 2 [8], MOGA 3 [9], MOEA/D 4 [10] for example) and used in a wide variety of applications [11,12,13,14,15,16,17,18,19,20,21,22,23,24]. Such algorithms mainly seek a set of Pareto optimal solutions that describe a Pareto front approximation.…”

Section: Introductionmentioning

confidence: 99%

Physical programming for preference driven evolutionary multi-objective optimization

Sanchis

Blasco

et al. 2014

Applied Soft Computing

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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.