Preference incorporation to solve many-objective airfoil design problems

Jáimes, Antonio López; Montano, Alfredo Arias; Coello, Carlos A. Coello

doi:10.1109/cec.2011.5949807

Cited by 24 publications

(5 citation statements)

References 11 publications

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“…The main idea is to incorporate the DM's preferences in the search space in order to distinguish between Pareto equivalent solutions that have the ability to evolve towards the ROI on problems involving more than 3 objectives [Ben Said et al 2010;Bechikh et al 2011]. Preference-based MOEAs have given many interesting results when addressing concrete problems in several engineering fields including software design by incorporating the designer preferences [Jaimes et al 2011;Siegmund et al 2012;Mkaouer et al 2013]. Thirdly, the new preference ordering relations is an alternative approach that takes into account additional information such as the rank of the particular solution regarding the different objectives and the related population [di Pierro 2007] in order to overcome the inability of differentiating between solutions with the increased of the number of objectives; however these methods do not necessarily agree with to the DMs preferences.…”

Section: Many-objective Search-based Software Engineeringmentioning

confidence: 99%

Many-Objective Software Remodularization Using NSGA-III

Mkaouer

Kessentini

Shaout

et al. 2015

ACM Trans. Softw. Eng. Methodol.

225

101

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Software systems nowadays are complex and difficult to maintain due to continuous changes and bad design choices. To handle the complexity of systems, software products are, in general, decomposed in terms of packages/modules containing classes that are dependent. However, it is challenging to automatically remodularize systems to improve their maintainability. The majority of existing remodularization work mainly satisfy one objective which is improving the structure of packages by optimizing coupling and cohesion. In addition, most of existing studies are limited to only few operation types such as move class and split packages. Many other objectives, such as the design semantics, reducing the number of changes and maximizing the consistency with development change history, are important to improve the quality of the software by remodularizing it. In this article, we propose a novel many-objective search-based approach using NSGA-III. The process aims at finding the optimal remodularization solutions that improve the structure of packages, minimize the number of changes, preserve semantics coherence, and reuse the history of changes. We evaluate the efficiency of our approach using four different open-source systems and one automotive industry project, provided by our industrial partner, through a quantitative and qualitative study conducted with software engineers.

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Section: Many-objective Search-based Software Engineeringmentioning

confidence: 99%

Many-Objective Software Remodularization Using NSGA-III

Mkaouer

Kessentini

Shaout

et al. 2015

ACM Trans. Softw. Eng. Methodol.

225

101

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“…A common one is that of a reference point (RP) -a point in the objective space, which represents a solution that is desired and seems possible to reach by the method [33][34][35]. An RP may be directly used for dominance relation [2,36], thus extending strict Pareto dominance. Of various RP-based methods, r-dominance [2] is particularly successful and flexible and has therefore been chosen to be applied here.…”

Section: Selected Preference-based Approachmentioning

confidence: 99%

Evolutionary Multi–Objective Weather Routing of Sailboats

Sobecka

Szlapczynski

Życzkowski

2020

Polish Maritime Research

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The paper presents a multi-objective method, which optimises the route of a sailboat. The presented method makes use of an evolutionary multi-objective (EMO) algorithm, which performs the optimisation according to three objective functions: total passage time, a sum of all course alterations made during the voyage and the average angle of heel. The last two of the objective functions reflect the navigator’s and passenger’s comfort, which may decrease with multiple turns or when experiencing an excessive heel angle for a long time. The optimisation process takes into account static bathymetry-related constraints as well as dynamic constraints related to the sailboat’s safety in changing wind and wave conditions. The method makes use of all of the above and finally returns an approximated Pareto set containing non-dominated solutions to the optimisation problem. The developed method has been implemented as a simulation application. The paper includes selected simulation results followed by their discussion.

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“…It was used to change Pareto dominance relation (e.g. gdominance [36], r-dominance [6], Chebyshev preference relation [26]), modify crowding distance (e.g. R-NSGA-II [18]), alter set quality indicator (e.g.…”

Section: Pmoeasmentioning

confidence: 99%

Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms

Yevseyeva

Basto‐Fernandes

et al. 2017

Lecture Notes in Computer Science

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Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preferencebased multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Prot´eg´e. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.

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Preference incorporation to solve many-objective airfoil design problems

Cited by 24 publications

References 11 publications

Many-Objective Software Remodularization Using NSGA-III

Many-Objective Software Remodularization Using NSGA-III

Evolutionary Multi–Objective Weather Routing of Sailboats

Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms

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