Multi-objective optimization approach to the ALSTOM gasifier problem

Griffin, Ian; Schröder, Peter; Chipperfield, A.J.; Fleming, Peter J.

doi:10.1243/0959651001540807

Cited by 25 publications

(13 citation statements)

References 7 publications

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“…These preferences were then used in a modified version of dominance which combines the concept of Pareto optimality with a preference operator to rank the candidate solutions according to both preference information and Pareto-dominance. This progressive preference articulation method has been used in a wide variety of engineering applications such as the optimisation of robust control strategies for gasifier power plants [20] and the design of lateral stability controllers for aircraft [31].…”

Section: Preference Articulation and Decision Makingmentioning

confidence: 99%

A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection

Rostami

O'Reilly

Shenfield

et al. 2015

Information Sciences

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This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. Published versionROSTAMI, Shahin, O'REILLY, Dean, SHENFIELD, Alex and BOWRING, Nicholas (2015). A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection. Information Sciences, 295, 494-520. Copyright and re-use policy AbstractThe incorporation of decision maker preferences is often neglected in the Evolutionary MultiObjective Optimisation (EMO) literature. The majority of the research in the field and the development of EMO algorithms is primarily focussed on converging to a Pareto optimal approximation close to or along the true Pareto front of synthetic test problems. However, when EMO is applied to real-world optimisation problems there is often a decision maker who is only interested in a portion of the Pareto front (the Region of Interest) which is defined by their expressed preferences for the problem objectives. In this paper a novel preference articulation operator for EMO algorithms is introduced (named the Weighted Z-score Preference Articulation Operator) with the flexibility of being incorporated a priori, a posteriori or progressively, and as either a primary or auxiliary fitness operator. The Weighted Z-score Preference Articulation Operator is incorporated into an implementation of the Multi-Objective Evolutionary Algorithm Based on Decomposition (named WZ-MOEA/D) and benchmarked against MOEA/D-DRA on a number of bi-objective and five-objective test problems with test cases containing preference information. After promising results are obtained when comparing WZ-MOEA/D to MOEA/D-DRA in the presence of decision maker preferences, WZ-MOEA/D is successfully applied to a real-world optimisation problem to optimise a classifier for Concealed Weapon Detection, producing better results than previously published classifier implementations.

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Section: Preference Articulation and Decision Makingmentioning

confidence: 99%

A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection

Rostami

O'Reilly

Shenfield

et al. 2015

Information Sciences

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“…tems design Multi-objective evolutionary algorithms have been successfully applied to many problems in the field of control systems engineering, from the offline design of robust controllers for a coal-fired gasification plant (Griffin et al, 2000) to model identification of nonlinear systems (Tan and Li, 2002). Whilst the majority of the applications of evolutionary multi-objective optimisation in control systems engineering have been in offline applications due to the iterative nature of the evolutionary design process, they have also been used in online applications applications such as hardware-in-the-loop tuning of a fuzzy logic based DC motor controller (Stewart et al, 2004).…”

Section: Multi-objective Evolutionary Algorithms In Control Sys-mentioning

confidence: 99%

Multi-objective Evolutionary Design of Robust Controllers on the Grid

Shenfield

Fleming

2011

IFAC Proceedings Volumes

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Coupling conventional controller design methods, model based controller synthesis and simulation, and multi-objective evolutionary optimisation methods frequently results in an extremely computationally expensive design process. However, the emerging paradigm of grid computing provides a powerful platform for the solution of such problems by providing transparent access to large-scale distributed high-performance compute resources. As well as substantially speeding up the time taken to find a single controller design satisfying a set of performance requirements this gridenabled design process allows a designer to effectively explore the solution space of potential candidate solutions. An example of this is in the multi-objective evolutionary design of robust controllers, where each candidate controller design has to be synthesised and the resulting performance of the compensated system evaluated by computer simulation. This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems which will then be demonstrated using and example of the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H ∞ loop shaping.

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“…The CAO was hybridized with NSGA-II in an optimization framework attempting to solve a benchmark control system design problem involving a gasifier [39]. This is a relatively expensive (computationally) problem (for example, 14 objectives) chosen to set the CAO computational effort in context with the computational demands of evaluating objectives for a realworld problem.…”

Section: Running Timesmentioning

confidence: 99%

“…Note that in [39], a larger population size and number of generations were deployed. However, the configuration used in this section was deemed sufficient since the goal of the presented experiments was to contrast the efficiency of NSGA-II and NSGA-II/CAO within a limited budget of objective evaluations rather than solving the gasifier problem itself.…”

Section: Running Timesmentioning

confidence: 99%