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Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified. [ABSTRACT FROM AUTHOR]

This review paper presents a comprehensive and full review of the so-called optimization algorithm, multi-verse optimizer algorithm (MOA), and reviews its main characteristics and procedures. This optimizer is a kind of the most recent powerful nature-inspired meta-heuristic algorithms, where it has been successfully implemented and utilized in several optimization problems in a variety of several fields, which are covered in this context, such as benchmark test functions, machine learning applications, engineering applications, network applications, parameters control, and other applications of MOA. This paper covers all the available publications that have been used MOA in its application, which are published in the literature including the variants of MOA such as binary, modifications, hybridizations, chaotic, and multi-objective. Followed by its applications, the assessment and evaluation, and finally the conclusions, which interested in the current works on the optimization algorithm, recommend potential future research directions. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a widely used and reliable machine learning algorithm. It has been successfully applied to many real-world problems, with remarkable results. However, it has also been observed that SVM computational complexity increases with the increase in data size. Although many SVM speed optimization techniques have been proposed in the literature, there is still need for further improvement on the performance speed and accuracy of this algorithm. In this paper, a boundary detection algorithm for SVM speed optimization called ant colony optimization instance selection algorithm (ACOISA) is proposed. ACOISA is inspired by edge selection in ant colony optimization (ACO) algorithm, and it performs two primary functions: boundary detection and boundary instance selection. In the algorithm, ACO is used for boundary detection and k-nearest neighbor algorithm is used for boundary instance selection. Different sets of experiments are carried out to validate the efficiency of the proposed technique. All the experiments were performed on 35 datasets containing three well-known e-fraud types (credit card fraud, email spam and phishing email) and 31 other datasets available at UCI data repository. The experimental results showed that the proposed technique efficiently improved SVM training speed in 100% of the datasets used for evaluation, without significantly affecting SVM classification quality. Furthermore, the Freidman's and Holm's post hoc tests were conducted to statistically validate the credibility of the results. The statistical test results revealed that the proposed technique is significantly faster, compared to the standard SVM and some existing instance selection techniques, in all cases. [ABSTRACT FROM AUTHOR]

This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybridized machine-learning framework called Extreme Learning Machine using self-adaptive multi-population-based Jaya algorithm for forecasting the currency exchange value. This learning technique attempts to take the advantages of generalization ability of Extreme Learning Machines (ELMs) along with the multi-population search scheme of Jaya optimization technique. This model can very well forecast the exchange price of USD–INR and USD–EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. Proposed model has been compared with original ELM and ELM-Jaya along with technical analysis method such as discrete wavelet neural network optimized with self-adaptive multi-population-based Jaya and the comparison of different performance measures like MAPE, Theil's U, ARV and MAE reveal that ELM using self-adaptive multi-population-based Jaya hybrid models possesses superior compared to the rest predictive models. Comparison of different features demonstrates technical indicators outperform other two features such as statistical measures and combination of both technical indicators and statistical measures. [ABSTRACT FROM AUTHOR]

In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed. Though at the outset the algorithm requires inverse of matrices, it has been shown that they would be obtained by performing matrix subtraction of the identity matrix by a scalar multiple of inverse of a positive semi-definite matrix that arises in the original formulation of TSVR. The algorithm can be easily implemented and does not need any optimization packages. To demonstrate its effectiveness, experiments were performed on well-known synthetic and real-world datasets. Similar or better generalization performance of the proposed method in less training time in comparison with the standard and twin support vector regression methods clearly exhibits its suitability and applicability. [ABSTRACT FROM AUTHOR]

In this paper, extreme learning machine (ELM) for ε-insensitive error loss function-based regression problem formulated in 2-norm as an unconstrained optimization problem in primal variables is proposed. Since the objective function of this unconstrained optimization problem is not twice differentiable, the popular generalized Hessian matrix and smoothing approaches are considered which lead to optimization problems whose solutions are determined using fast Newton-Armijo algorithm. The main advantage of the algorithm is that at each iteration, a system of linear equations is solved. By performing numerical experiments on a number of interesting synthetic and real-world datasets, the results of the proposed method are compared with that of ELM using additive and radial basis function hidden nodes and of support vector regression (SVR) using Gaussian kernel. Similar or better generalization performance of the proposed method on the test data in comparable computational time over ELM and SVR clearly illustrates its efficiency and applicability. [ABSTRACT FROM AUTHOR]

As a novel learning algorithm for single-hidden-layer feedforward neural networks, extreme learning machines (ELMs) have been a promising tool for regression and classification applications. However, it is not trivial for ELMs to find the proper number of hidden neurons due to the nonoptimal input weights and hidden biases. In this paper, a new model selection method of ELM based on multi-objective optimization is proposed to obtain compact networks with good generalization ability. First, a new leave-one-out (LOO) error bound of ELM is derived, and it can be calculated with negligible computational cost once the ELM training is finished. Furthermore, the hidden nodes are added to the network one-by-one, and at each step, a multi-objective optimization algorithm is used to select optimal input weights by minimizing this LOO bound and the norm of output weight simultaneously in order to avoid over-fitting. Experiments on five UCI regression data sets are conducted, demonstrating that the proposed algorithm can generally obtain better generalization performance with more compact network than the conventional gradient-based back-propagation method, original ELM and evolutionary ELM. [ABSTRACT FROM AUTHOR]

The harmony search (HS) method is a popular meta-heuristic optimization algorithm, which has been extensively employed to handle various engineering problems. However, it sometimes fails to offer a satisfactory convergence performance under certain circumstances. In this paper, we propose and study a hybrid HS approach, HS-PBIL, by merging the HS together with the population-based incremental learning (PBIL). Numerical simulations demonstrate that our HS-PBIL is well capable of outperforming the regular HS method in dealing with nonlinear function optimization and a practical wind generator optimization problem. [ABSTRACT FROM AUTHOR]