29,974 research outputs found
Full Elite Sets for Multi-Objective Optimisation
Copyright © 2002 Springer. The final publication is available at link.springer.com5th International Conference on Adaptive Computing in Design and Manufacture (ACDM 2002), Exeter, UK, 16-18 April, 2002Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of frontal estimates found using the full archive is illustrated on test problems
Real-time co-ordinated scheduling using a genetic algorithm
Real-time co-ordination is an emerging approach to operational engineering management aimed at being more comprehensive and widely applicable than existing approaches. Schedule management is a key characteristic of operational co-ordination related to managing the planning and dynamic assignment of tasks to resources, and the enactment of the resulting schedules, throughout a changeable process. This paper presents the application of an agent-oriented system, called the Design Co-ordination System, to an industrial case study in order to demonstrate the appropriate use of a genetic algorithm for the purpose of real-time scheduling. The application demonstrates that real-time co-ordinated scheduling can provide significant reductions in time to complete the computational design process
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The management of intelligence-assisted finite element analysis technology
Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
Visualization of Free Search Process
The article presents visualization of an adaptive method for optimization called Free Search and gives a
description of methodology used. Implemented tasks are illustrated with relevant graphics. Benefits of having 3D
graphical interface are in opportunity to observe and track the changes and behavior of this algorithm during the search
process. Technologies and tools used for visualization are explained and discussed
From 3D Models to 3D Prints: an Overview of the Processing Pipeline
Due to the wide diffusion of 3D printing technologies, geometric algorithms
for Additive Manufacturing are being invented at an impressive speed. Each
single step, in particular along the Process Planning pipeline, can now count
on dozens of methods that prepare the 3D model for fabrication, while analysing
and optimizing geometry and machine instructions for various objectives. This
report provides a classification of this huge state of the art, and elicits the
relation between each single algorithm and a list of desirable objectives
during Process Planning. The objectives themselves are listed and discussed,
along with possible needs for tradeoffs. Additive Manufacturing technologies
are broadly categorized to explicitly relate classes of devices and supported
features. Finally, this report offers an analysis of the state of the art while
discussing open and challenging problems from both an academic and an
industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and
Innovation action; Grant agreement N. 68044
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