In the era of digital boom, single classifier cannot perform well in various datasets. Ensemble classifier aims to bridge this performance gap by combining multiple classifiers of diverse characteristics to get better...
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In the era of digital boom, single classifier cannot perform well in various datasets. Ensemble classifier aims to bridge this performance gap by combining multiple classifiers of diverse characteristics to get better generalization. But classifier selection highly depends on the dataset, and its efficiency degrades tremendously due to the presence of irrelevant features. Feature selection aids the performance of classifier by removing those irrelevant features. Initially, we have proposed a bi-objective genetic algorithm-based feature selection method (FSBOGA), where nonlinear, uniform, hybrid cellular automata are used to generate an initial population. objective functions are defined using lower bound approximation of rough set theory and Kullback-Leibler divergence method of information theory to select unambiguous and informative features. The replacement strategy for creation of next-generation population is based on the Pareto optimal solution with respect to both the objective functions. Next, a novel bi-objective genetic algorithm-based ensemble classification method (CCBOGA) is devised to ensemble the individual classifiers designed using obtained reduced datasets. It is observed that the constructed ensemble classifier performs better than the individual classifiers. The performances of proposed FSBOGA and CCBOGA are investigated on some popular datasets and compared with the state-of-the-art algorithms to demonstrate their effectiveness.
Production efficiency, product quality and cost control are the focus issues that manufacturers are extremely concerned about. Multi-product manufacturing systems can mitigate uncertainty and enhance flexibility in th...
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Production efficiency, product quality and cost control are the focus issues that manufacturers are extremely concerned about. Multi-product manufacturing systems can mitigate uncertainty and enhance flexibility in the production process. In order to comprehensively explore the characteristics of multi-product manufacturing systems, this paper selects a two-product manufacturing system with setup time as the research object, and introduces a machine maintenance mechanism combining preventive maintenance(PM) with post-failure repair to analyze reliability and optimization problems of the manufacturing system. The Markov process approach is utilized for system modeling and analysis. Based on matrix equations, an iterative solution is employed to calculate the steady-state probabilities. This method yields production management indexes such as system throughput, the average work-in-process of the system and system mission completion rate. Additionally, it helps define and derive reliability indexes of the machine, including mean available time(MAT) and mean time before first unavailable(MTBFU). Based on numerical experiments, the influence degree and underlying reasons of each parameter on the performance indexes of the system and the machine are analyzed. The bi-objective genetic algorithm is used to determine optimal operating cost solutions for the manufacturing system.
The optimization of the membrane electrode assembly (MEA) is a crucial aspect in improving the performance of high-temperature proton exchange membrane fuel cells (HT-PEMFCs). Through experimental validation, a three-...
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The optimization of the membrane electrode assembly (MEA) is a crucial aspect in improving the performance of high-temperature proton exchange membrane fuel cells (HT-PEMFCs). Through experimental validation, a three-dimensional HT-PEMFC computational fluid dynamics (CFD) model was established to simulate the transport of charge, mass, and momentum across various porous layers. A deep neural network (DNN) was designed to quantify the influence of porous layer structures on cells performance and determined the optimal solution of the MEA parameters for different Pt loadings. The training data of DNN were generated using 11 porous layer structural parameters selected via a Monte Carlo method, and their corresponding current densities predicted by the validated HT-PEMFC CFD model. The DNN model showed an average relative error of only 0.76% in predicting current density when compared to CFD simulations. Using a biobjectivegeneticalgorithm (GA) in conjunction with the DNN model, the optimized cell power densities at a voltage of 0.4 V and Pt loadings of 0.3 mg/cm2 and 0.5 mg/cm2 are 18% and 9% higher than the maximum values reported in the literature, respectively. The proposed approach combining DNN model and GA can serve as a useful guide for the design of HT-PEMFC in practical applications.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a bi-objective genetic algorithm fo...
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ISBN:
(纸本)9783642226052
A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a bi-objective genetic algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.
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