This study introduces a hybrid multi-objective evolutionary algorithm (MOEA) for the optimization of aircraft control system design. The strategy suggested here is composed mainly of two stages. The first stage consis...
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Rolling bearings play a significant role in the operation of rotating machinery. Finding bearing faults in the early stage can not only keep machinery running safely but also avoid economic loss. Traditional machine l...
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Model structure selection is crucial in system identification and data-driven modelling. Many spurious candidate variables can influence the determination of model structures due to the lack of prior knowledge of the ...
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Cargo screening is an important process to inspect illegal contraband, such as drugs, nuclear materials, weapons and explosives at seaports and airports. A great deal of research has been carried out to address the pr...
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Camera calibration is a necessary step in 3D modeling in order to extract metric information from images. Computed camera parameters are used in a lot of computer vision applications which involves geometric computati...
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Manufacturing efficiency and transport operations are being significantly improved by mobile robots. As the implementation of a configurable, lightweight, and stateof-the-art robotic system is required for current man...
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In this paper, a new method for solving the fault detection and isolation (FDI) problem in general nonlinear stochastic systems is proposed. The proposed method is based on adaptive Monte Carlo filter and likelihood r...
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Blood is a vital source for delivering oxygen and nutrients to trillions of cells in the body;this makes the function of the cardiovascular system essential to our existence. In the last few years, research interests ...
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In smart healthcare, binary classification is one of the most important tasks for disease or clinical outcome prediction. Machine learning (ML) methods have great potential to discover knowledge from data. While there...
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Decomposition based approaches are known to perform well on many-objective problems when a suitable set of weights is provided. However, providing a suitable set of weights a priori is difficult. This study proposes a...
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ISBN:
(纸本)9781450319645
Decomposition based approaches are known to perform well on many-objective problems when a suitable set of weights is provided. However, providing a suitable set of weights a priori is difficult. This study proposes a novel algorithm: preference-inspired co-evolutionary algorithm using weights (PICEA-w), which co-evolves a set of weights with the usual population of candidate solutions during the search process. The co-evolution enables suitable sets of weights to be constructed along the optimization process, thus guiding the candidate solutions toward the Pareto optimal front. Experimental results show PICEA-w performs better than algorithms embedded with random or uniform weights.
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