Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization

Mattson, Christopher A.; Messac, Achille

doi:10.1023/b:opte.0000048538.35456.45

Cited by 188 publications

(123 citation statements)

References 44 publications

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“…In [2], an addendum is incorporated into the Pareto front notion to differentiate design concepts. A Pareto front is defined given a design concept (or simply, a concept) which is an idea about how to solve a given MOP.…”

Section: Multi-objective Optimisation Design Proceduresmentioning

confidence: 99%

“…When dealing with a MOP, we usually seek for a Pareto optimal solution [1] in which the objectives have been improved as much as possible without giving anything in exchange. According to [2], there are two main approaches to solve an optimisation statement for a MOP: the aggregate objective function (AOF) or the generate-first choose-later (GFCL) approach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Reynoso-Meza

Blasco

Sanchis

et al. 2014

Control Engineering Practice

118

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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.

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Section: Multi-objective Optimisation Design Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Reynoso-Meza

Blasco

Sanchis

et al. 2014

Control Engineering Practice

118

View full text Add to dashboard Cite

show abstract

“…The competing objectives and resulting trade-off are identified. The optimal system configurations are compared and illustrated in the form of a Pareto optimal frontier [48], which shows graphically the optimisation solutions where any individual better-off would result in at least one individual worse-off. …”

Section: Thermoenvironomic Modellingmentioning

confidence: 99%

Oil and gas platforms with steam bottoming cycles: System integration and thermoenvironomic evaluation

et al. 2014

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“…uncertainty in the control vari-able runaway boundaries, and the inherent random disturbances of the control variable setpoint. 5,8,9,[32][33][34][35][36] On the other hand, the plant optimal operating policy should be considered as a trade-off between economic, environmental, and safety objectives. By using a new "safe operation criterion" introduced by Maria and Dan,[10][11][12][13][14] based on the sum of two failure probability indices related to uncertainty in the reactor runaway boundaries and random disturbances in the operating parameters, a multi-objective optimization can be formulated by defining the sustainability by simultaneously considering technological, economic, and safety constraints.…”

Section: Introductionmentioning

confidence: 99%

Derivation of Pareto Optimal Operating Policies Based on Safety Indices for a Catalytic Multi-tubular Reactor Used for Nitrobenzene Hydrogenation

Maria

Khwayyir

Dinculescu

2016

Chem.Biochem.Eng.Q.

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When highly exothermic/hazardous reactions are conducted in the presence of parametric uncertainty, derivation of optimal operating policies for a chemical reactor has to simultaneously consider several objectives of sustainability. The paper uses an original methodology to generate the Pareto optimal solutions when reactor productivity and safety objectives (expressed in probabilistic terms) are simultaneously considered using the process and reactor model in a simple way, in the presence of technological constraints, uncertainty in safety boundaries, and random fluctuations in control variables. An example is provided for the industrial fixed-bed tubular reactor used for the catalytic hydrogenation of nitrobenzene to aniline in vapour-phase.

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Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization

Cited by 188 publications

References 44 publications

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

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

Oil and gas platforms with steam bottoming cycles: System integration and thermoenvironomic evaluation

Derivation of Pareto Optimal Operating Policies Based on Safety Indices for a Catalytic Multi-tubular Reactor Used for Nitrobenzene Hydrogenation

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