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Article
Publication date: 3 July 2017

Anand Amrit, Leifur Leifsson and Slawomir Koziel

This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to…

Abstract

Purpose

This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level.

Design/methodology/approach

Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate.

Findings

Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day.

Originality/value

The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

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Article
Publication date: 7 March 2016

Slawomir Koziel and Adrian Bekasiewicz

Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling…

Abstract

Purpose

Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues.

Design/methodology/approach

Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures.

Findings

It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures.

Research limitations/implications

The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components.

Originality/value

The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

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Article
Publication date: 4 April 2016

Fowei Wang, Bo Shen, Shaoyuan Sun and Zidong Wang

The purpose of this paper is to improve the accuracy of the facial expression recognition by using genetic algorithm (GA) with an appropriate fitness evaluation function…

Abstract

Purpose

The purpose of this paper is to improve the accuracy of the facial expression recognition by using genetic algorithm (GA) with an appropriate fitness evaluation function and Pareto optimization model with two new objective functions.

Design/methodology/approach

To achieve facial expression recognition with high accuracy, the Haar-like features representation approach and the bilateral filter are first used to preprocess the facial image. Second, the uniform local Gabor binary patterns are used to extract the facial feature so as to reduce the feature dimension. Third, an improved GA and Pareto optimization approach are used to select the optimal significant features. Fourth, the random forest classifier is chosen to achieve the feature classification. Subsequently, some comparative experiments are implemented. Finally, the conclusion is drawn and some future research topics are pointed out.

Findings

The experiment results show that the proposed facial expression recognition algorithm outperforms ones in the existing literature in terms of both the actuary and computational time.

Originality/value

The GA and Pareto optimization algorithm are combined to select the optimal significant feature. To improve the accuracy of the facial expression recognition, the GA is improved by adjusting an appropriate fitness evaluation function, and a new Pareto optimization model is proposed that contains two objective functions indicating the achievements in minimizing within-class variations and in maximizing between-class variations.

Details

Assembly Automation, vol. 36 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 1 February 2006

Hui‐Yuan Fan, Jouni Lampinen and Yeshayahou Levy

To present and validate a new differential evolution (DE) method for multi‐objective optimization method.

Abstract

Purpose

To present and validate a new differential evolution (DE) method for multi‐objective optimization method.

Design/methodology/approach

A new selection scheme was designed to replace the existing one in DE to enable DE applicable to either single objective or multi‐objective optimizations.

Findings

The new method was validated with three simple multi‐objective optimization problems. The simulation results show that the approach is capable of generating an approximated Pareto‐front for each selected problem. The new DE method was used to optimize a prototype air mixer subject to two objective functions to be minimized. The results demonstrate that the new DE approach can handle this practical multi‐objective problem successfully.

Originality/value

The new method is an easy‐to‐implement evolutionary method and has the potential for application for any complicated engineering optimizations.

Details

Engineering Computations, vol. 23 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

Content available
Article
Publication date: 30 August 2021

Kailun Feng, Shiwei Chen, Weizhuo Lu, Shuo Wang, Bin Yang, Chengshuang Sun and Yaowu Wang

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the…

Abstract

Purpose

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

Design/methodology/approach

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

Findings

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

Originality/value

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

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Article
Publication date: 2 January 2019

Marimuthu Kannimuthu, Benny Raphael, Ekambaram Palaneeswaran and Ananthanarayanan Kuppuswamy

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

Abstract

Purpose

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

Design/methodology/approach

A case study approach identified the activity execution modes in building construction projects in India to support multi-mode resource-constrained project scheduling. The data required to compute time, cost and quality of each activity are compiled from real construction projects. A binary integer-programming model has been developed to perform multi-objective optimization and identify Pareto optimal solutions. The RR-PARETO3 algorithm was used to identify the best compromise trade-off solutions. The effectiveness of the proposed framework is demonstrated through sample case study projects.

Findings

Results show that good compromise solutions are obtained through multi-objective optimization of time, cost and quality.

Research limitations/implications

Case study data sets were collected only from eight building construction projects in India.

Practical implications

It is feasible to adopt multi-objective optimization in practical construction projects using time, cost and quality as the objectives; Pareto surfaces help to quantify relationships among time, cost and quality. It is shown that cost can be reduced by increasing the duration, and quality can be improved only by increasing the cost.

Originality/value

The use of different activity execution modes compiled from multiple projects in optimization is illustrated, and good compromise solutions for the multi-mode resource-constrained project scheduling problems using multi-objective optimization are identified.

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Article
Publication date: 26 July 2021

Giovani Gaiardo Fossati, Letícia Fleck Fadel Miguel and Walter Jesus Paucar Casas

This study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes…

Abstract

Purpose

This study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time.

Design/methodology/approach

Unlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time.

Findings

The results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety.

Originality/value

Despite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found.

Details

Engineering Computations, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 5 September 2016

Murat Caner, Chris Gerada, Greg Asher and Tolga Özer

The purpose of this paper is to investigate Halbach array effects in surface mounted permanent magnet machine (SMPM) in terms of both self-sensing and torque capabilities…

Abstract

Purpose

The purpose of this paper is to investigate Halbach array effects in surface mounted permanent magnet machine (SMPM) in terms of both self-sensing and torque capabilities. A comparison between a conventional SMPM, which has radially magnetized rotor, and a Halbach machine has been carried out.

Design/methodology/approach

The geometric parameters of the two machines have been optimized using genetic algorithm (GA) with looking Pareto. The performance of the machines’ geometry has been calculated by finite element analysis (FEA) software, and two parametric machine models have been realized in Matlab coupled with the FEA and GA toolboxes. Outer volume of the machine, thus copper loss per volume has been kept constant. The Pareto front approach, which simultaneously considers looks two aims, has been used to provide the trade-off between the torque and sensorless performances.

Findings

The two machines’ results have been compared separately for each loading condition. According to the results, the superiority of the Halbach machine has been shown in terms of sensorless capability compromising torque performance. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine.

Originality/value

A Halbach machine design optimization has been presented using Pareto optimal set which provides a trade-off comparison between two aims without using weightings. These are sensorless performance and torque capability. There is no such a work about sensorless capability of the Halbach type SMPM in the literature.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 35 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 18 November 2011

Benny Raphael

The purpose of this paper is to improve current design processes by proposing a new approach based on multi‐criteria optimization of the designed asset. Management of…

Abstract

Purpose

The purpose of this paper is to improve current design processes by proposing a new approach based on multi‐criteria optimization of the designed asset. Management of design in construction projects is a complex task since it involves collaboration between professionals in multiple disciplines. Traditionally, designers work with a single solution at a time which is iteratively modified according to the view points of all the consultants. This results in sub‐optimal solutions. A multi‐criteria approach is able to accommodate diverse view points of specialist consultants in a construction project, aiming at a better optimized building system.

Design/methodology/approach

The shortcomings of current design practices are analyzed based on a literature review. It is found that current approaches involving single objective optimization or Pareto optimization are not adequate for supporting collaborative design processes. A new approach to managing multiple objectives in design is proposed. This involves performing multi‐objective optimization, presenting a population of good solutions to the design consultants and selecting the best solution through an algorithm called RR‐PARETO2 (Relaxed‐Restricted Pareto) filtering. A software tool with a graphical user interface was developed. An example of the design of a building façade is taken to evaluate the application of this approach.

Findings

The paper provides empirical evidence that a multi‐objective optimization approach is able to provide support for the task of accommodating multiple viewpoints in design. The proposed methodology allows navigation through the solution space and pruning it visually by applying constraints. It is shown that the RR‐PARETO2 is able to select a good compromise solution with the best trade‐offs among all the objectives.

Originality/value

The idea of visualizing and filtering a population of design solutions has been proposed by design researchers for a long time, but is not currently adopted in practice in construction projects. The idea of collaborative filtering of the solution space according to the viewpoints of all the consultants by visually applying constraints on design variables and objectives is a new concept, the ultimate aim being a better balanced built asset. This is the first time the RR‐PARETO2 algorithm has been applied to building design.

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Article
Publication date: 12 March 2018

K. Shankar and Akshay S. Baviskar

The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is…

Abstract

Purpose

The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems.

Design/methodology/approach

This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population.

Findings

The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm.

Practical implications

The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper.

Social implications

The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives.

Originality/value

This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 11 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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