In this article, we conducted a new hybrid method between Non-dominated Sorting Genetic algorithm II (NSGA-III) and SPEA/r (HNSGA-III&SPEA/r). This method is implemented to find the optimal values of the powertrai...
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In this article, we conducted a new hybrid method between Non-dominated Sorting Genetic algorithm II (NSGA-III) and SPEA/r (HNSGA-III&SPEA/r). This method is implemented to find the optimal values of the powertrain mount system stiffness parameters. This is the task of finding multi-objective optimization involving six simultaneous optimization goals: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/r has proposed with the integration of Strength Pareto evolutionary algorithm-based reference direction for Multi-objective (SPEA/r) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/r is more efficient than the typical SPEA/r and NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/r is simulated. It proved the potential of the HNSGA-III&SPEA/r for powertrain mount system stiffness parameter optimization problem.
In this study, a new methodology, hybrid Strength Pareto Evolutionary algorithmreference Direction (SPEA/r) with Deep Neural Network (HDNN&SPEA/r), has been developed to achieve cost optimization of stiffness par...
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In this study, a new methodology, hybrid Strength Pareto Evolutionary algorithmreference Direction (SPEA/r) with Deep Neural Network (HDNN&SPEA/r), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/r is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/r. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/r is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/r is simulated, respectively. It proved the potential of the HDNN&SPEA/r for stiffness parameter for powertrain mount systems optimization problem.
Background: Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic...
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Background: Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms. results: Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the r source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change. Conclusions: The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.
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