Handling integrated quantitative and qualitative search space in a real world optimisation problem

Oduguwa, Victor; Tiwari, Ashutosh; Roy, Rajkumar

doi:10.1109/cec.2003.1299808

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…The above procedure of using fuzzy modeling in EMO to represent qualitativeness has been proposed by Oduguwa et al [7]. They suggested that for every quantitative solution lying on the Pareto front, there exists a corresponding qualitative evaluation expressing the designer's opinion about the problem, and that this qualitative evaluation stands in a unique relation with the quantitative solution.…”

Section: E a Review Of Previous Approaches For Integrating Multidimementioning

confidence: 99%

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

Brintrup

Ramsden

Tokuda

et al. 2008

IEEE Trans. Evol. Computat.

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This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics.

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Section: E a Review Of Previous Approaches For Integrating Multidimementioning

confidence: 99%

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

Brintrup

Ramsden

Tokuda

et al. 2008

IEEE Trans. Evol. Computat.

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“…In Equation.2, the parameters defined a n-dimension space, which is the solution space for the target problem, and the optimizing process will be operated in it for the best solutions [7]~ [9] .…”

Section: Solution Space Dimension Reduction Methodsmentioning

confidence: 99%

Product innovation model based on user attention information

Sun

Wang

Liu

2010

2010 Sixth International Conference on Natural Computation

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Regarding the quantitative application of user attention information(UAI) in product innovation design, the paper studied the types, objects, and algorithms of UAI and its aiding model in product developing process. The product parameters' element attention curves are profiled, together with the element attention coefficients calculated by the noise degree evaluating method. According to element attention coefficients, product code is classed into segments. The product design process is fulfilled by searching in the solution space units with interactive genetic algorithms(IGA). The model extracts the parameters' importance factors from user choice information as the criteria for product parameter design planning. The model provides a quantitative mechanism for using UAI in product design process. The method was applied in product styling example for the validation.

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“…Completing the problem definition: It has been reported that there may be cases where an accurate numerical representation of search space is not readily available [5]. In this case, qualitative information can close the loop of problem definition by taking advantage of human approximate reasoning and qualitative judgment.…”

Section: On the Need For Qualitativenessmentioning

confidence: 99%

“…In summary, Oduguwa et al [5] has proposed modeling, and hence quantifying, qualitative objectives using FS and creating the problem as a multi-objective one. Parmee and Cvetkovic [6], has modeled designer preferences using FS and proposed an agent based negotiation system.…”

Section: Domeuni-dimensionally Qualitativementioning

confidence: 99%

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Integrated Qualitativeness in Design by Multi-Objective Optimization and Interactive Evolutionary Computation

Brintrup¹,

Ramsden²,

Tiwari³

2005 IEEE Congress on Evolutionary Computation

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The concept of qualitativeness in design is an important one, and needs to be incorporated in the optimization process for a number of reasons outlined in this paper. Interactive Evolutionary Computation and Fuzzy Systems are two of the widely used approaches for handling qualitativeness in design optimization. This paper classifies the types of qualitativeness observed in design optimization, makes the case for their necessity, and proposes a novel framework for handling them, combining the two approaches in an evolutionary multi-objective optimization platform. Two components of the framework are tested using the floor-planning problem, and observations are reported. Future work is defined on the development of the framework.

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Handling integrated quantitative and qualitative search space in a real world optimisation problem

Cited by 4 publications

References 11 publications

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

Product innovation model based on user attention information

Integrated Qualitativeness in Design by Multi-Objective Optimization and Interactive Evolutionary Computation

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