Community Detection Based on Genetic Algorithm Using Local Structural Similarity

Guo, Xuchao; Su, Jie; Zhou, Honggang; Liu, Chengqi; Cao, Jing; Li, Lin

doi:10.1109/access.2019.2939864

Cited by 17 publications

(4 citation statements)

References 54 publications

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“…This will help limit premature convergence in stochastic optimization algorithms. A proposed new population initialization approach is based on feature weights of ReliefF method [41] and roulette wheel selection used in binary optimization algorithm for feature selection [42], [43]. In this approach, features are selected in proportion to their weights.…”

Section: ) Population Initialization Based On Feature Weightsmentioning

confidence: 99%

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

Lee

2021

IEEE Access

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This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the potential features. An enhancement of the binary particle swarm optimization algorithm through population initialization strategy based on feature weights, new updating mechanism, and the screening and replacing process create a new and effective feature selection method that improves classification accuracy and reduces data size. The optimal feature subset is provided separately for artificial neural networks, and support vector machine classifier for the final recognition task. In multiple case studies, the proposed feature selection method is evaluated against the benchmark data sets and shows performance comparable to that of other peer competitors. The effectiveness of the proposed bearing fault diagnosis model is verified on different testbeds and achieves high accuracy and robustness under noise conditions. In addition, experimental results are compared with existing fault diagnostic models, showing the high possibility of the proposed bearing fault diagnosis model.

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Section: ) Population Initialization Based On Feature Weightsmentioning

confidence: 99%

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

Lee

2021

IEEE Access

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“…(1) Selection Operation Roulette wheel selection is a frequently used method in GAs [43]. The selection probability of the individual is directly proportional to its fitness; this may lead to excessive concentration of similar individuals with high fitness in the population after the selection operation, to reduce the search space and increase the probability of falling into the local optimum.…”

Section: Genetic Operationsmentioning

confidence: 99%

Solution to Solid Wood Board Cutting Stock Problem

et al. 2021

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In the production process for wooden furniture, the raw material costs account for more than 50% of furniture costs, and the utilization rate of raw materials depends mainly on the layout scheme. Therefore, a reasonable layout is an important measure to reduce furniture costs. This paper investigates the solid wood board cutting stock problem (CSP) and establishes an optimization model, with the goal of the highest possible utilization rate for original boards. An ant colony-immune genetic algorithm (ACIGA) is designed to solve this model. The solutions of the ant colony algorithm are used as the initial population of the immune genetic algorithm, and the optimal solution is obtained using the immune genetic algorithm after multiple iterations are transformed into the accumulation of global pheromones, which improves the search ability and ensures the solution quality. The layout process of the solid wood board is abstracted into the construction process of the solution. At the same time, in order to prevent premature convergence, several improved methods, such as a global pheromone hybrid update and adaptive crossover probability, are proposed. Comparative experiments are designed to verify the feasibility and effectiveness of the ACIGA, and the experimental results show that the ACIGA has better solution precision and global search ability compared with the ant colony algorithm (ACA), genetic algorithm (GA), grey wolf optimizer (GWO), and polar bear optimization (PBO). The utilization rate increased by more than 2.308%, which provides effective theoretical and methodological support for furniture enterprises to improve economic benefits.

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“…Step 3: keep the set of DAPs corresponding to the minimum fitness, defined as D min . According to the roulette wheel selection (RWS) method [24], reselect Q-1 DAPs. On this basis, the one-point crossover method is used to perform pair-wise cross-over operations on the sets of D min and Q-1 DAPs, and set the cross-over probability P c [25].…”

Section: Acga-dapsmentioning

confidence: 99%

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

et al. 2021

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High-resolution remote sensing (HRRS) images, when used for building detection, play a key role in urban planning and other fields. Compared with the deep learning methods, the method based on morphological attribute profiles (MAPs) exhibits good performance in the absence of massive annotated samples. MAPs have been proven to have a strong ability for extracting detailed characterizations of buildings with multiple attributes and scales. So far, a great deal of attention has been paid to this application. Nevertheless, the constraints of rational selection of attribute scales and evidence conflicts between attributes should be overcome, so as to establish reliable unsupervised detection models. To this end, this research proposes a joint optimization and fusion building detection method for MAPs. In the pre-processing step, the set of candidate building objects are extracted by image segmentation and a set of discriminant rules. Second, the differential profiles of MAPs are screened by using a genetic algorithm and a cross-probability adaptive selection strategy is proposed; on this basis, an unsupervised decision fusion framework is established by constructing a novel statistics-space building index (SSBI). Finally, the automated detection of buildings is realized. We show that the proposed method is significantly better than the state-of-the-art methods on HRRS images with different groups of different regions and different sensors, and overall accuracy (OA) of our proposed method is more than 91.9%.

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Community Detection Based on Genetic Algorithm Using Local Structural Similarity

Cited by 17 publications

References 54 publications

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

Solution to Solid Wood Board Cutting Stock Problem

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

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