An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm

Reliability assessment of phased-mission systems under random shocks

This paper introduces a new phenomenological cumulative damage rule to predict damage and fatigue life under variable amplitude loading. The rule combines a residual S-N curve approach and a material memory concept to describe the damage accumulation behavior. The residual S-N curve slope is regarded as a variable with respect to the loading history. The change in slope is then used as a damage measure and quantified by a material memory degeneration parameter. This model improves the traditional linear damage rule by taking the load-level dependence and loading sequence effect into account, which still preserves its superiority. A series of non-uniform fatigue loading protocols are used to demonstrate the effectiveness of the proposed model. The prediction results using the proposed model are more accurate than those using three popular damage models. Moreover, several common characteristics and fundamental properties of the chosen fatigue models are extracted and discussed.

A New Cumulative Fatigue Damage Rule Based on Dynamic Residual S-N Curve and Material Memory Concept

Abstract We improve convergence speed by two orders of magnitude and the global exploration capabilities of particle swarm optimization (PSO) through targeted position-mutated elitism (TPME). The proposed fast-converging TPME operator requires a fitness-based classification technique to categorize the particles. The introduced classification is motivated by its simplicity, low memory requirements, and automated termination criteria based on convergence. The three key innovations address particle classification, elitism, and mutation in the cognitive and social model. PSO-TPME is benchmarked against five popular PSO variants for multi-dimensional functions, which are extensively adopted in the optimization field, In particular, the convergence accuracy, convergence speed, and the capability to find global minima are investigated. The statistical error is assessed by numerous repetitions. The simulations confirmed that in ten of the thirteen investigated functions, the proposed PSO variant outperforms other variants in terms of convergence rate and accuracy by at least two orders of magnitude. On the other hand, the simulations demonstrated the early exploration capabilities of PSO-TPME in all tested functions. In the first ten iterations, PSO-TPME outperformed all the investigated PSO variants by at least two orders of magnitude. 

/pdf/a-fast-converging-particle-swarm-optimization-through-27dm6pdo.pdf

A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules. 

/pdf/research-on-lung-nodule-recognition-algorithm-based-on-deep-1xew5dxc.pdf

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Fault diagnosis method based on supervised particle swarm optimization classification algorithm

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Fault diagnosis method based on supervised particle swarm optimization classification algorithm