Starch foam material performance prediction based on a radial basis function artificial neural network trained by bare-bones particle swarm optimization with an adaptive disturbance factor

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.

Predicting mechanical performance of starch-based foam materials

Variations in mixture proportions of plasticizers, additives, and crosslinking agents have significant impacts on mechanical performance of starch-based foam materials. In particular, starch/ethylene-vinyl acetate (EVA) foam materials have been developed with improved mechanical strength by optimizing the formulation. There is a lack of numerical correlations that could help analyze the effects of components and provide a predictive method for future research. In this study, we develop simple and accurate predictions for tensile strength and resilience based on mixture proportions of components for starch-based/EVA foam materials. The models constructed might be used to help design mixture proportions of starch-based foam materials. By combining optimization results from the Taguchi method and machine learning approaches, it is expected that more quantitative data can be extracted from fewer experimental trials at the same time.