A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer

Abstract

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-functionbased weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. Experiment results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

“…In particular, the modified GAN has shown better performance than the conventional GAN. In our future work, we aim to: 1) develop an automatic hyperparameter selection scheme for GAN-based crack detection system based on evolutionary computation [31][32][33][34][35][36][37]; 2) apply our crack detection system to other thermography NDT tasks; 3) design a novel control strategy to enhance the detection performance of the crack detection system [38][39][40][41][42][43][44][45][46]; and 4) explore the dynamic behavior of the ECPT-based NDT process and study the thermography features based on signal processing and control theory [47][48][49][50][51][52][53][54][55][56].…”

Task Allocation on Layered Multiagent Systems: When Evolutionary Many-Objective Optimization Meets Deep Q-Learning

“…13. The optimal makespan comparison between two kinds of methods deep learning within different cooperation environment and various agent types for various targets [6], [25]; 2) solving the uncertain task allocation problem in a dynamic environment by using some novel optimization methods [2], [10], [35]- [38], [53]; and 3) the task allocation problem on MASs subject to engineering-oriented complexities [40], [46], [59], [62]- [64].…”

An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing

“…Experiment results have shown that the improved GAN outperforms the standard GAN for reconstructing the magnetic optical images, and the developed GAN-based crack detection method has demonstrated satisfactory performance in detecting the cracks in magnetic optical images. In the future, we aim to (1) employ our proposed GANbased crack detection for other NDT methods such as thermal imagery detection, ultrasound detection and X-ray imaging detection; (2) deploy evolutionary computation methods to optimize the parameter selection in the GAN-based crack detection system [24][25][26][27]; (3) employ signal processing and state estimation methods to investigate the stability of the NDT process [17,[19][20][21]28,34,35,42,46,54]; and (4) design an advanced control strategy to improve the detection accuracy and detection rate of the GAN-based crack detection system [14,18,33,47,50,52,55,56].…”