The benefits of adaptive parametrization in multi-objective Tabu Search optimization

Ghisu, Tiziano; Parks, Geoffrey T.; Jaeggi, Daniel; Jarrett, Jerome P.; Clarkson, P. John

doi:10.1080/03052150903564882

Cited by 23 publications

(8 citation statements)

References 20 publications

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“…This enabled the designer to manipulate various combination of the design variables and better understand different design scenarios [43,44]. The optimisation was performed using the Multi-Objective Tabu Search (MOTS) algorithm [67][68][69], chosen for its suitability for this type of complex aerodynamic design problem [68,70,71]. However, for confirmation, the authors used optimisation results for this design case to be compared with the leading multi-objective Genetic Algorithmic (GA), NSGA-II.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

Azabi

Chai

Kipouros

2019

MAKE

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This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

Azabi

Chai

Kipouros

2019

MAKE

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“…Piperni et al [59] and Zhang et al [60] have argued that the level of fidelity that should be delivered by the models is mostly determined at the development stage, which the design process aims to enhance. On the other side, the optimisation was performed using the Multi-Objective Tabu Search (MOTS) algorithm, chosen for its suitability for this type of complex aerodynamic design problem [35][36][37].…”

Section: Particles Selection Schema On I-mopso Interfacementioning

confidence: 99%

“…Use of MOTS has rapidly expanded in recent years as one of the efficient meta-heuristic techniques dealing with complex real-world problems [36]. Ghisu et al have argued that researchers previously paid little attention towards the TS algorithm compared to other multi-objective meta-heuristics, but recently TS has gained attention for its use in aerodynamic shape design problems [37].…”

Section: Introductionmentioning

confidence: 99%

The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAV

2019

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In this work, an interactive optimisation framework—a combination of a low fidelity flow solver, Athena Vortex Lattice (AVL), and an interactive Multi-Objective Particle Swarm Optimisation (MOPSO)—is proposed for aerodynamic shape design optimisation of any aerial vehicle platform. This paper demonstrates the benefits of interactive optimisation—reduction of computational time with high optimality levels. Progress towards the most preferred solutions is made by having the Decision Maker (DM) periodically provide preference information once the MOPSO iterations are underway. By involving the DM within the optimisation process, the search is directed to the region of interest, which accelerates the process. The flexibility and efficiency of undertaking optimisation interactively have been demonstrated by comparing the interactive results with the non-interactive results of an optimum design case obtained using Multi-Objective Tabu Search (MOTS) for the Aegis UAV. The obtained results show the superiority of using an interactive approach for the aerodynamic shape design, compared to posteriori approaches. By carrying out the optimisation using interactive MOPSO it was shown to be possible to obtain similar results to non-interactive MOTS with only half the evaluations. Moreover, much of the usual complexity of post-data-analysis with posteriori approaches is avoided, since the DM is involved in the search process.

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“…These parameters are presented in Table 1 and detailed description of their meaning can be found in. 6,13…”

Section: Description Of the Four-objective Optimisation Test Casementioning

confidence: 99%

“…The second variant, PCMOTS, has been developed and presented by Ghisu et al, 6,7 who applied it successfully to a number of test functions taken from the ZDT 21 and DTLZ 2 families and to a real-world aerodynamic test case, the preliminary design optimisation of the compressor core of an aero-engine. This implementation is based on Principal Components' Analysis, and the aim is to identify an optimal reorientation of the design parameters and to prioritise the most energetic of these rotated design parameters.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Higher-order Multi-objective Optimisation for 3D Aerodynamic Shape Design

Kipouros¹,

Ghisu²,

Parks³

et al. 2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials ConferenceSelf Cite

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We investigate the performance of different variants of a suitably tailored Tabu Search optimisation algorithm on a higher-order design problem. We consider four objective functions to describe the performance of a compressor stator row, subject to a number of equality and inequality constraints. The same design problem has been previously investigated through single-, bi-and three-objective optimisation studies. However, in this study we explore the capabilities of enhanced variants of our Multi-objective Tabu Search (MOTS) optimisation algorithm in the context of detailed 3D aerodynamic shape design. It is shown that with these enhancements to the local search of the MOTS algorithm we can achieve a rapid exploration of complicated design spaces, but there is a trade-off between speed and the quality of the trade-off surface found. Rapidly explored design spaces reveal the extremes of the objective functions, but the compromise optimum areas are not very well explored. However, there are ways to adapt the behaviour of the optimiser and maintain both a very efficient rate of progress towards the global optimum Pareto front and a healthy number of design configurations lying on the trade-off surface and exploring the compromise optimum regions. These compromise solutions almost always represent the best qualitative balance between the objectives under consideration. Such enhancements to the effectiveness of design space exploration make engineering design optimisation with multiple objectives and robustness criteria ever more practicable and attractive for modern advanced engineering design. Finally, new research questions are addressed that highlight the trade-offs between intelligence in optimisation algorithms and acquisition of qualitative information through computational engineering design processes that reveal patterns and relations between design parameters and objective functions, but also speed versus optimum quality.

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The benefits of adaptive parametrization in multi-objective Tabu Search optimization

Cited by 23 publications

References 20 publications

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAV

An Investigation of Higher-order Multi-objective Optimisation for 3D Aerodynamic Shape Design

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