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Dr. Timoleon Kipouros
Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom

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Review
Published: 17 April 2021 in Journal of Global Optimization
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This is a review of the book ‘Deterministic Global Optimization: An Introduction to the Diagonal Approach’.

ACS Style

Timoleon Kipouros. Review on the monograph Deterministic Global Optimization: An Introduction to the Diagonal Approach, Springer, 2017, written by Yaroslav D. Sergeyev and Dmitri E. Kvasov. Journal of Global Optimization 2021, 1 -2.

AMA Style

Timoleon Kipouros. Review on the monograph Deterministic Global Optimization: An Introduction to the Diagonal Approach, Springer, 2017, written by Yaroslav D. Sergeyev and Dmitri E. Kvasov. Journal of Global Optimization. 2021; ():1-2.

Chicago/Turabian Style

Timoleon Kipouros. 2021. "Review on the monograph Deterministic Global Optimization: An Introduction to the Diagonal Approach, Springer, 2017, written by Yaroslav D. Sergeyev and Dmitri E. Kvasov." Journal of Global Optimization , no. : 1-2.

Journal article
Published: 11 December 2019 in Journal of Engineering for Gas Turbines and Power
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Recent research on turbomachinery design and analysis for supercritical Carbon Dioxide (sCO2) power cycles has relied on Computational Fluid Dynamics. This has produced a large number of works whose approach is mostly case-specific, rather than of general application to sCO2 turbomachinery design. As opposed to such approach, this work explores the aerodynamic performance of compressor blade cascades operating on air and supercritical CO2 with the main objective to evaluate the usual aerodynamic parameters of the cascade for variable boundary conditions and geometries, enabling 'full' or 'partial' similarity. The results present both the global performance of the cascades and certain features of the local flow (trailing edge and wake). The discussion also highlights the mechanical limitations of the analysis (forces exerted on the blades), which is the main restriction to applying similarity laws to extrapolate the experi- ence gained through decades of work on air turbomachinery to the new working fluid. This approach is a step towards the understanding and appropriate formulation of a multi-objective optimisation problem for the design of such turbomachinery components where sCO2 is used as the operating fluid. With this objective, the paper aims to identify and analyse what would be expected if a common description of such computational design problems similar to those where air is the working fluid were used

ACS Style

Carlos Tello; David Sánchez; Alejandro Muñoz; Timoleon Kipouros; A. Mark Savill. Impact of Fluid Substitution On the Performance of an Axial Compressor Blade Cascade Working with Supercritical Carbon Dioxide. Journal of Engineering for Gas Turbines and Power 2019, 142, 1 .

AMA Style

Carlos Tello, David Sánchez, Alejandro Muñoz, Timoleon Kipouros, A. Mark Savill. Impact of Fluid Substitution On the Performance of an Axial Compressor Blade Cascade Working with Supercritical Carbon Dioxide. Journal of Engineering for Gas Turbines and Power. 2019; 142 (1):1.

Chicago/Turabian Style

Carlos Tello; David Sánchez; Alejandro Muñoz; Timoleon Kipouros; A. Mark Savill. 2019. "Impact of Fluid Substitution On the Performance of an Axial Compressor Blade Cascade Working with Supercritical Carbon Dioxide." Journal of Engineering for Gas Turbines and Power 142, no. 1: 1.

Journal article
Published: 08 April 2019 in Aerospace
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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.

ACS Style

Yousef Azabi; Al Savvaris; Timoleon Kipouros. The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAV. Aerospace 2019, 6, 42 .

AMA Style

Yousef Azabi, Al Savvaris, Timoleon Kipouros. The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAV. Aerospace. 2019; 6 (4):42.

Chicago/Turabian Style

Yousef Azabi; Al Savvaris; Timoleon Kipouros. 2019. "The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAV." Aerospace 6, no. 4: 42.

Journal article
Published: 04 April 2019 in Machine Learning and Knowledge Extraction
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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.

ACS Style

Yousef Azabi; Al Savvaris; Timoleon Kipouros. Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV. Machine Learning and Knowledge Extraction 2019, 1, 552 -574.

AMA Style

Yousef Azabi, Al Savvaris, Timoleon Kipouros. Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV. Machine Learning and Knowledge Extraction. 2019; 1 (2):552-574.

Chicago/Turabian Style

Yousef Azabi; Al Savvaris; Timoleon Kipouros. 2019. "Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV." Machine Learning and Knowledge Extraction 1, no. 2: 552-574.

Original article
Published: 16 October 2018 in Engineering with Computers
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Topology optimization has proven to be viable for use in the preliminary phases of real world design problems. Ultimately, the restricting factor is the computational expense since a multitude of designs need to be considered. This is especially imperative in such fields as aerospace, automotive and biomedical, where the problems involve multiple physical models, typically fluids and structures, requiring excessive computational calculations. One possible solution to this is to implement codes on massively parallel computer architectures, such as graphics processing units (GPUs). The present work investigates the feasibility of a GPU-implemented lattice Boltzmann method for multi-physics topology optimization for the first time. Noticeable differences between the GPU implementation and a central processing unit (CPU) version of the code are observed and the challenges associated with finding feasible solutions in a computational efficient manner are discussed and solved here, for the first time on a multi-physics topology optimization problem. The main goal of this paper is to speed up the topology optimization process for multi-physics problems without restricting the design domain, or sacrificing considerable performance in the objectives. Examples are compared with both standard CPU and various levels of numerical precision GPU codes to better illustrate the advantages and disadvantages of this implementation. A structural and fluid objective topology optimization problem is solved to vary the dependence of the algorithm on the GPU, extending on the previous literature that has only considered structural objectives of non-design dependent load problems. The results of this work indicate some discrepancies between GPU and CPU implementations that have not been seen before in the literature and are imperative to the speed-up of multi-physics topology optimization algorithms using GPUs.

ACS Style

David J. Munk; Timoleon Kipouros; Gareth Vio. Multi-physics bi-directional evolutionary topology optimization on GPU-architecture. Engineering with Computers 2018, 35, 1059 -1079.

AMA Style

David J. Munk, Timoleon Kipouros, Gareth Vio. Multi-physics bi-directional evolutionary topology optimization on GPU-architecture. Engineering with Computers. 2018; 35 (3):1059-1079.

Chicago/Turabian Style

David J. Munk; Timoleon Kipouros; Gareth Vio. 2018. "Multi-physics bi-directional evolutionary topology optimization on GPU-architecture." Engineering with Computers 35, no. 3: 1059-1079.

Journal article
Published: 12 October 2018 in Designs
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In this work, we investigate the computational design of a typical S-Duct that is found in the literature. We model the design problem as a shape optimization study. The design parameters describe the 3D geometrical changes to the shape of the S-Duct and we assess the improvements to the aerodynamic behavior by considering two objective functions: the pressure losses and the swirl. The geometry management is controlled with the Free-Form Deformation (FFD) technique, the analysis of the flow is performed using steady-state computational fluid dynamics (CFD), and the exploration of the design space is achieved using the heuristic optimization algorithm Tabu Search (MOTS). The results reveal potential improvements by 14% with respect to the pressure losses and by 71% with respect to the swirl of the flow. These findings exceed by a large margin the optimality level that was achieved by other approaches in the literature. Further investigation of a range of optimum geometries is performed and reported with a detailed discussion.

ACS Style

Alessio D’Ambros; Timoleon Kipouros; Pavlos Zachos; Mark Savill; Ernesto Benini. Computational Design Optimization for S-Ducts. Designs 2018, 2, 36 .

AMA Style

Alessio D’Ambros, Timoleon Kipouros, Pavlos Zachos, Mark Savill, Ernesto Benini. Computational Design Optimization for S-Ducts. Designs. 2018; 2 (4):36.

Chicago/Turabian Style

Alessio D’Ambros; Timoleon Kipouros; Pavlos Zachos; Mark Savill; Ernesto Benini. 2018. "Computational Design Optimization for S-Ducts." Designs 2, no. 4: 36.

Research article
Published: 10 October 2018 in Complexity
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The transition to a secure low-carbon system is raising a set of uncertainties when planning the path to a reliable decarbonised supply. The electricity sector is committing large investments in the transmission and distribution sector upon 2050 in order to ensure grid resilience. The cost and limited flexibility of traditional approaches to 11 kV network reinforcement threaten to constrain the uptake of low-carbon technologies. This paper investigates the suitability and cost-effectiveness of smart grid techniques along with traditional reinforcements for the 11 kV electricity distribution network, in order to analyse expected investments up to 2050 under different DECC demand scenarios. The evaluation of asset planning is based on an area of study in Milton Keynes (East Midlands, United Kingdom), being composed of six 11 kV primaries. To undertake this, the analysis used a revolutionary new model tool for electricity distribution network planning, called scenario investment model (SIM). Comprehensive comparisons of short- and long-term evolutionary investment planning strategies are presented. The work helps electricity network operators to visualise and design operational planning investments providing bottom-up decision support.

ACS Style

Jesus Nieto-Martin; Timoleon Kipouros; Mark Savill; Jennifer Woodruff; Jevgenijs Butans. Technoeconomic Distribution Network Planning Using Smart Grid Techniques with Evolutionary Self-Healing Network States. Complexity 2018, 2018, 1 -18.

AMA Style

Jesus Nieto-Martin, Timoleon Kipouros, Mark Savill, Jennifer Woodruff, Jevgenijs Butans. Technoeconomic Distribution Network Planning Using Smart Grid Techniques with Evolutionary Self-Healing Network States. Complexity. 2018; 2018 ():1-18.

Chicago/Turabian Style

Jesus Nieto-Martin; Timoleon Kipouros; Mark Savill; Jennifer Woodruff; Jevgenijs Butans. 2018. "Technoeconomic Distribution Network Planning Using Smart Grid Techniques with Evolutionary Self-Healing Network States." Complexity 2018, no. : 1-18.

Conference paper
Published: 14 September 2018 in EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization
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Multi-objective optimization has become an invaluable tool in engineering design. One class of solutions to the multi-objective optimization problem is known as the Pareto frontier. The Pareto frontier is made up of a group of solutions known as Pareto optimal solutions. These solutions are optimal in the sense that any improvement in one design objective must come with the worsening of at least one other. Therefore, the Pareto frontier plays a vital role in engineering design, since it defines the trade-offs between conflicting objectives. Methods exist that can automatically generate a set of Pareto solutions from which the final design can be chosen. For such an approach to be successful, the generated set must truly be representative of the complete design space. This paper offers a new phase in the development of the smart normal constraint bi-directional evolutionary optimization method, which is a recently developed approach that allows the efficient and effective generation of smart Pareto sets to multi-objective topology optimization problems. Currently, only bi-objective topology optimization problems can be solved with this method. Therefore, in this paper the method is generalized to solve topology optimization problems with any number of objectives. This is demonstrated on an example having three objectives.

ACS Style

David J. Munk; Timoleon Kipouros; Gareth A. Vio. A Generalized SNC-BESO Method for Multi-objective Topology Optimization. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization 2018, 3 -14.

AMA Style

David J. Munk, Timoleon Kipouros, Gareth A. Vio. A Generalized SNC-BESO Method for Multi-objective Topology Optimization. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. 2018; ():3-14.

Chicago/Turabian Style

David J. Munk; Timoleon Kipouros; Gareth A. Vio. 2018. "A Generalized SNC-BESO Method for Multi-objective Topology Optimization." EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization , no. : 3-14.

Conference paper
Published: 24 June 2018 in 2018 Multidisciplinary Analysis and Optimization Conference
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ACS Style

Fabio Crescenti; Timoleon Kipouros; A Mark Savill. Loading and planform shape influence on the wing structural layout through topology optimization. 2018 Multidisciplinary Analysis and Optimization Conference 2018, 1 .

AMA Style

Fabio Crescenti, Timoleon Kipouros, A Mark Savill. Loading and planform shape influence on the wing structural layout through topology optimization. 2018 Multidisciplinary Analysis and Optimization Conference. 2018; ():1.

Chicago/Turabian Style

Fabio Crescenti; Timoleon Kipouros; A Mark Savill. 2018. "Loading and planform shape influence on the wing structural layout through topology optimization." 2018 Multidisciplinary Analysis and Optimization Conference , no. : 1.

Journal article
Published: 01 June 2018 in Finite Elements in Analysis and Design
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ACS Style

David J. Munk; Timoleon Kipouros; Gareth A. Vio; Geoffrey T. Parks; Grant P. Steven. On the effect of fluid-structure interactions and choice of algorithm in multi-physics topology optimisation. Finite Elements in Analysis and Design 2018, 145, 32 -54.

AMA Style

David J. Munk, Timoleon Kipouros, Gareth A. Vio, Geoffrey T. Parks, Grant P. Steven. On the effect of fluid-structure interactions and choice of algorithm in multi-physics topology optimisation. Finite Elements in Analysis and Design. 2018; 145 ():32-54.

Chicago/Turabian Style

David J. Munk; Timoleon Kipouros; Gareth A. Vio; Geoffrey T. Parks; Grant P. Steven. 2018. "On the effect of fluid-structure interactions and choice of algorithm in multi-physics topology optimisation." Finite Elements in Analysis and Design 145, no. : 32-54.

Journal article
Published: 04 January 2018 in Aerospace
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In this paper, a practical optimization framework and enhanced strategy within an industrial setting are proposed for solving large-scale structural optimization problems in aerospace. The goal is to eliminate the difficulties associated with optimization problems, which are mostly nonlinear with numerous mixed continuous-discrete design variables. Particular emphasis is placed on generating good initial starting points for the search process and in finding a feasible optimum solution or improving the chances of finding a better optimum solution when traditional techniques and methods have failed. The efficiency and reliability of the proposed strategy were demonstrated through the weight optimization of different metallic and composite laminated wingbox structures. The results show the effectiveness of the proposed procedures in finding an optimized solution for high-dimensional search space cases with a given level of accuracy and reasonable computational resources and user efforts. Conclusions are also inferred with regards to the sensitivity of the optimization results obtained with respect to the choice of different starting values for the design variables, as well as different optimization algorithms in the optimization process.

ACS Style

Odeh Dababneh; Timoleon Kipouros; James F. Whidborne. Application of an Efficient Gradient-Based Optimization Strategy for Aircraft Wing Structures. Aerospace 2018, 5, 3 .

AMA Style

Odeh Dababneh, Timoleon Kipouros, James F. Whidborne. Application of an Efficient Gradient-Based Optimization Strategy for Aircraft Wing Structures. Aerospace. 2018; 5 (1):3.

Chicago/Turabian Style

Odeh Dababneh; Timoleon Kipouros; James F. Whidborne. 2018. "Application of an Efficient Gradient-Based Optimization Strategy for Aircraft Wing Structures." Aerospace 5, no. 1: 3.

Review
Published: 01 January 2018 in Aerospace Science and Technology
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ACS Style

Odeh Dababneh; Timoleon Kipouros. A review of aircraft wing mass estimation methods. Aerospace Science and Technology 2018, 72, 256 -266.

AMA Style

Odeh Dababneh, Timoleon Kipouros. A review of aircraft wing mass estimation methods. Aerospace Science and Technology. 2018; 72 ():256-266.

Chicago/Turabian Style

Odeh Dababneh; Timoleon Kipouros. 2018. "A review of aircraft wing mass estimation methods." Aerospace Science and Technology 72, no. : 256-266.

Conference paper
Published: 06 December 2017 in Advances in Structural and Multidisciplinary Optimization
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To date the design of structures via topology optimisation methods has mainly focused on single-objective problems. However, real-world design problems usually involve several different objectives, most of which counteract each other. Therefore, designers typically seek a set of Pareto optimal solutions, a solution for which no other solution is better in all objectives, which capture the trade-off between these objectives. This set is known as a smart Pareto set. Currently, only the weighted sums method has been used for generating Pareto fronts with topology optimisation methods. However, the weighted sums method is unable to produce evenly distributed smart Pareto sets. Furthermore, evenly distributed weights have been shown to not produce evenly spaced solutions. Therefore, the weighted sums method is not suitable for generating smart Pareto sets. Recently, the smart normal constraints method has been shown to be capable of directly generating smart Pareto sets. This work presents an updated smart normal constraint method, which is combined with a bi-directional evolutionary structural optimisation algorithm for multi-objective topology optimisation. The smart normal constraints method has been modified by further restricting the feasible design space for each optimisation run such that dominant and redundant points are not found. The algorithm is tested on several different structural optimisation problems. A number of different structural objectives are analysed, namely compliance, dynamic and buckling objectives. Therefore, the method is shown to be capable of solving various types of multi-objective structural optimisation problems. The goal of this work is to show that smart Pareto sets can be produced for complex topology optimisation problems. Furthermore, this research hopes to highlight the gap in the literature of topology optimisation for multi-objective problems.

ACS Style

David J. Munk; Gareth A. Vio; Grant P. Steven; Timoleon Kipouros. Producing Smart Pareto Sets for Multi-objective Topology Optimisation Problems. Advances in Structural and Multidisciplinary Optimization 2017, 145 -162.

AMA Style

David J. Munk, Gareth A. Vio, Grant P. Steven, Timoleon Kipouros. Producing Smart Pareto Sets for Multi-objective Topology Optimisation Problems. Advances in Structural and Multidisciplinary Optimization. 2017; ():145-162.

Chicago/Turabian Style

David J. Munk; Gareth A. Vio; Grant P. Steven; Timoleon Kipouros. 2017. "Producing Smart Pareto Sets for Multi-objective Topology Optimisation Problems." Advances in Structural and Multidisciplinary Optimization , no. : 145-162.

Journal article
Published: 01 November 2017 in Journal of Computational Physics
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ACS Style

David J. Munk; Timoleon Kipouros; Gareth Vio; Grant P. Steven; Geoffrey T. Parks. Topology optimisation of micro fluidic mixers considering fluid-structure interactions with a coupled Lattice Boltzmann algorithm. Journal of Computational Physics 2017, 349, 11 -32.

AMA Style

David J. Munk, Timoleon Kipouros, Gareth Vio, Grant P. Steven, Geoffrey T. Parks. Topology optimisation of micro fluidic mixers considering fluid-structure interactions with a coupled Lattice Boltzmann algorithm. Journal of Computational Physics. 2017; 349 ():11-32.

Chicago/Turabian Style

David J. Munk; Timoleon Kipouros; Gareth Vio; Grant P. Steven; Geoffrey T. Parks. 2017. "Topology optimisation of micro fluidic mixers considering fluid-structure interactions with a coupled Lattice Boltzmann algorithm." Journal of Computational Physics 349, no. : 11-32.

Research paper
Published: 11 August 2017 in Structural and Multidisciplinary Optimization
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To date the design of structures using topology optimization methods has mainly focused on single-objective problems. Since real-world design problems typically involve several different objectives, most of which counteract each other, it is desirable to present the designer with a set of Pareto optimal solutions that capture the trade-off between these objectives, known as a smart Pareto set. Thus far only the weighted sums and global criterion methods have been incorporated into topology optimization problems. Such methods are unable to produce evenly distributed smart Pareto sets. However, recently the smart normal constraint method has been shown to be capable of directly generating smart Pareto sets. Therefore, in the present work, an updated smart Normal Constraint Method is combined with a Bi-directional Evolutionary Structural Optimization (SNC-BESO) algorithm to produce smart Pareto sets for multiobjective topology optimization problems. Two examples are presented, showing that the Pareto solutions found by the SNC-BESO method make up a smart Pareto set. The first example, taken from the literature, shows the benefits of the SNC-BESO method. The second example is an industrial design problem for a micro fluidic mixer. Thus, the problem is multi-physics as well as multiobjective, highlighting the applicability of such methods to real-world problems. The results indicate that the method is capable of producing smart Pareto sets to industrial problems in an effective and efficient manner.

ACS Style

David J. Munk; Timoleon Kipouros; Gareth Vio; Geoffrey T. Parks; Grant P. Steven. Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method. Structural and Multidisciplinary Optimization 2017, 57, 665 -688.

AMA Style

David J. Munk, Timoleon Kipouros, Gareth Vio, Geoffrey T. Parks, Grant P. Steven. Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method. Structural and Multidisciplinary Optimization. 2017; 57 (2):665-688.

Chicago/Turabian Style

David J. Munk; Timoleon Kipouros; Gareth Vio; Geoffrey T. Parks; Grant P. Steven. 2017. "Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method." Structural and Multidisciplinary Optimization 57, no. 2: 665-688.

Chapter
Published: 29 April 2017 in Econometrics for Financial Applications
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Evolutionary algorithms (EAs) have been used to tackle non-linear multi-objective optimisation (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising EA (MOOEA) that uses self-adaptive mutation and crossover, and which is applied to optimisation of an airfoil, for minimisation of drag and maximisation of lift coefficients. The MOOEA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.

ACS Style

John M. Oliver; Timoleon Kipouros; Mark Savill. Multi-objective Optimisation by Self-adaptive Evolutionary Algorithm. Econometrics for Financial Applications 2017, 111 -134.

AMA Style

John M. Oliver, Timoleon Kipouros, Mark Savill. Multi-objective Optimisation by Self-adaptive Evolutionary Algorithm. Econometrics for Financial Applications. 2017; ():111-134.

Chicago/Turabian Style

John M. Oliver; Timoleon Kipouros; Mark Savill. 2017. "Multi-objective Optimisation by Self-adaptive Evolutionary Algorithm." Econometrics for Financial Applications , no. : 111-134.

Conference paper
Published: 05 January 2017 in 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
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ACS Style

Spyridon G. Kontogiannis; Mark Savill; Timoleon Kipouros. A Multi-Objective Multi-Fidelity framework for global optimization. 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 2017, 1 .

AMA Style

Spyridon G. Kontogiannis, Mark Savill, Timoleon Kipouros. A Multi-Objective Multi-Fidelity framework for global optimization. 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 2017; ():1.

Chicago/Turabian Style

Spyridon G. Kontogiannis; Mark Savill; Timoleon Kipouros. 2017. "A Multi-Objective Multi-Fidelity framework for global optimization." 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference , no. : 1.

Journal article
Published: 01 January 2017 in Procedia Computer Science
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Engineering design is typically a complex process that involves finding a set of designs satisfying various performance criteria. As a result, optimisation algorithms dealing with only single-objective are not sufficient to deal with many real-life problems. Meanwhile, scientific workflows have been shown to be an effective technology for automating and encapsulating scientific processes. While optimisation algorithms have been integrated into workflow tools, they are generally single-objective. This paper first presents our latest development to incorporate multi-objective optimisation algorithms into scientific workflows. We demonstrate the efficacy of these capabilities with the formulation of a three-objective aerodynamics optimisation problem. We target to improve the aerodynamic characteristics of a typical 2D airfoil profile considering also the laminar-turbulent transition location for more accurate estimation of the total drag. We deploy two different heuristic optimisation algorithms and compare the preliminary results

ACS Style

Hoang Anh Nguyen; Zane Van Iperen; Sreekanth Raghunath; David Abramson; Timoleon Kipouros; Sandeep Somasekharan. Multi-objective optimisation in scientific workflow. Procedia Computer Science 2017, 108, 1443 -1452.

AMA Style

Hoang Anh Nguyen, Zane Van Iperen, Sreekanth Raghunath, David Abramson, Timoleon Kipouros, Sandeep Somasekharan. Multi-objective optimisation in scientific workflow. Procedia Computer Science. 2017; 108 ():1443-1452.

Chicago/Turabian Style

Hoang Anh Nguyen; Zane Van Iperen; Sreekanth Raghunath; David Abramson; Timoleon Kipouros; Sandeep Somasekharan. 2017. "Multi-objective optimisation in scientific workflow." Procedia Computer Science 108, no. : 1443-1452.

Proceedings article
Published: 21 November 2016 in 2016 IEEE Congress on Evolutionary Computation (CEC)
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In this paper we explore uncertainty quantification and management in an industrial context. We use a multielement airfoil section at take-off conditions, the so called Airbus Test Case A, for the demonstration of our methods. We also try to identify through this case study the elements that are necessary to synthesise in order to move towards the characterisation of multi-disciplinary complexities of uncertainty. Multi-point optimisation, using a multi-objective Tabu Search, is first used to demonstrate a significant decrease in sensitivity to variations in incidence and flap deflection. Sigma-point, non-intrusive spectral projection, and non-intrusive point collocation methods of uncertainty quantification are then applied to the same test case with uncertain incidence. An additional uncertain parameter, the flap deflection, is then added for a final optimisation using non-intrusive point collocation. Though mean increases in lift to drag ratio are comparable, a systematic reduction in attainable robustness is observed when compared to the single uncertainty cases. We also emphasise on the importance of visualising the multi-dimensional data that are produced from such computational design studies, and the possibility to identify hidden characteristics and correlations between parameters and performance characteristics. We use Parallel Coordinates and interactively we can reveal such relationships. We believe a suitable synthesis of all these tools and methods can assist towards the identification of implicit correlations between uncertainties that are produced when different disciplines are considered simultaneously for industrial design.

ACS Style

J. Loxham; Timoleon Kipouros; A. M. Savill. Towards uncertainty quantification and management in multi-disciplinary design optimisation. 2016 IEEE Congress on Evolutionary Computation (CEC) 2016, 885 -892.

AMA Style

J. Loxham, Timoleon Kipouros, A. M. Savill. Towards uncertainty quantification and management in multi-disciplinary design optimisation. 2016 IEEE Congress on Evolutionary Computation (CEC). 2016; ():885-892.

Chicago/Turabian Style

J. Loxham; Timoleon Kipouros; A. M. Savill. 2016. "Towards uncertainty quantification and management in multi-disciplinary design optimisation." 2016 IEEE Congress on Evolutionary Computation (CEC) , no. : 885-892.

Conference paper
Published: 03 November 2016 in 2016 International Joint Conference on Neural Networks (IJCNN)
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A potential area of difficulty for Multi-Objective Optimisation of industrial problems is a class of problems where the majority of the objective space violates blackbox constraints. The difficult arises because potential solutions that violate blackbox constraints provide no information beyond their infeasibility. They provide neither meaningful information about their objective values nor about the degree to which the constraint is violated (or even in some cases which constraint is violated). This means that they do not help to find valid solutions (except by elimination) which, in turn, reduces the early stages of optimisation to effective guesswork until some feasible solutions are found. In this work, we attempt to reduce this problem by using a Decision Tree to identify and repair infeasible solutions by learning the underlying constraints on each parameter. We propose three potential Pre-Repair Methods and compare them on a modified case study of an airfoil lift/drag optimisation problem. Note that no optimisation was done; instead the goal was to decide if the repair methodologies were suitable in the problem space. We used two baselines: not using a Decision Tree, and only using a Decision Tree to identify potentially infeasible solutions for complete regeneration. All three of our proposed methods outperformed the baselines at a statistically significant level of confidence of 0.001.

ACS Style

Timothy Rawlins; Andrew Lewis; Timoleon Kipouros. Repairing blackbox constraint violations in Multi-Objective Optimisation by use of decision trees. 2016 International Joint Conference on Neural Networks (IJCNN) 2016, 1104 -1111.

AMA Style

Timothy Rawlins, Andrew Lewis, Timoleon Kipouros. Repairing blackbox constraint violations in Multi-Objective Optimisation by use of decision trees. 2016 International Joint Conference on Neural Networks (IJCNN). 2016; ():1104-1111.

Chicago/Turabian Style

Timothy Rawlins; Andrew Lewis; Timoleon Kipouros. 2016. "Repairing blackbox constraint violations in Multi-Objective Optimisation by use of decision trees." 2016 International Joint Conference on Neural Networks (IJCNN) , no. : 1104-1111.