A recent set of overused population-based methods have been published in recent years. Despite their popularity, most of them have uncertain, immature performance, partially done verifications, similar overused metaphors, similar immature exploration and exploitation components and operations, and an insecure tradeoff between exploration and exploitation trends in most of the new real-world cases. Therefore, all users need to extensively modify and adjust their operations based on main evolutionary methods to reach faster convergence, more stable balance, and high-quality results. To move the optimization community one step ahead toward more focus on performance rather than change of metaphor, a general-purpose population-based optimization technique called Hunger Games Search (HGS) is proposed in this research with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. The proposed HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods. The effectiveness of HGS was verified by comparing HGS with a comprehensive set of popular and advanced algorithms on 23 well-known optimization functions and the IEEE CEC 2014 benchmark test suite. Also, the HGS was applied to several engineering problems to demonstrate its applicability. The results validate the effectiveness of the proposed optimizer compared to popular essential optimizers, several advanced variants of the existing methods, and several CEC winners and powerful differential evolution (DE)-based methods abbreviated as LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods in handling many single-objective problems. We designed this open-source population-based method to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity. The method is very flexible and scalable to be extended to fit more form of optimization cases in both structural aspects and application sides. This paper's source codes, supplementary files, Latex and office source files, sources of plots, a brief version and pseudocode, and an open-source software toolkit for solving optimization problems with Hunger Games Search and online web service for any question, feedback, suggestion, and idea on HGS algorithm will be available to the public at https://aliasgharheidari.com/HGS.html .

Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts

Breast cancer (BC) is one of the primary causes of cancer death among women. Early detection of BC allows patients to receive appropriate treatment, thus increasing the possibility of survival. In this work, a new deep-learning (DL) model based on the transfer-learning (TL) technique is developed to efficiently assist in the automatic detection and diagnosis of the BC suspected area based on two techniques namely 80–20 and cross-validation. DL architectures are modeled to be problem-specific. TL uses the knowledge gained during solving one problem in another relevant problem. In the proposed model, the features are extracted from the mammographic image analysis- society (MIAS) dataset using a pre-trained convolutional neural network (CNN) architecture such as Inception V3, ResNet50, Visual Geometry Group networks (VGG)-19, VGG-16, and Inception-V2 ResNet. Six evaluation metrics for evaluating the performance of the proposed model in terms of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC curve (AUC) has been chosen. Experimental results show that the TL of the VGG16 model is powerful for BC diagnosis by classifying the mammogram breast images with overall accuracy, sensitivity, specificity, precision, F-score, and AUC of 98.96%, 97.83%, 99.13%, 97.35%, 97.66%, and 0.995, respectively for 80–20 method and 98.87%, 97.27%, 98.2%, 98.84%, 98.04%, and 0.993 for 10-fold cross-validation method.

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A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed as SGOA, which combines simulated annealing (SA) mechanism with the original GOA that is a natural optimizer widely used in finance, medical and other fields, and receives more promising results based on grasshopper behavior. To compare performance of the SGOA and other algorithms, an investigation to select CEC2017 benchmark function as the test set was carried out. Also, the Friedman assessment was performed to check the significance of the proposed method against other counterparts. In comparison with ten meta-heuristic algorithms such as differential evolution (DE), the proposed SGOA can rank first in the CEC2017, and also ranks first in comparison with ten advanced algorithms. The simulation results reveal that the SA strategy notably improves the exploration and exploitation capacity of GOA. Moreover, the SGOA is also applied to engineering problems and parameter optimization of the kernel extreme learning machine (KELM). After optimizing the parameters of KELM using SGOA, the model was applied to two datasets, Cleveland Heart Dataset and Japanese Bankruptcy Dataset, and they achieved an accuracy of 79.2% and 83.5%, respectively, which were better than the KELM model obtained other algorithms. In these practical applications, it is indicated that the proposed SGOA can provide effective assistance in settling complex optimization problems with impressive results.

SGOA: annealing-behaved grasshopper optimizer for global tasks

https://doi.org/10.1016/j.compbiomed.2022.105618

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Boosting slime mould algorithm for parameter identification of photovoltaic models

Video Deblurring via Spatiotemporal Pyramid Network and Adversarial Gradient Prior

The goal of unpaired image-to-image translation is to learn a mapping from a source domain to a target domain without using any labeled examples of paired images. This problem can be solved by learning the conditional distribution of source images in the target domain. A major limitation of existing unpaired image-to-image translation algorithms is that they generate untruthful images which are overcolored and lack details, while the translation of realistic images must be rich in details. To address this limitation, in this article, we propose a random reconstructed unpaired image-to-image translation (RRUIT) framework by generative adversarial network, which uses random reconstruction to preserve the high-level features in the source and adopts an adversarial strategy to learn the distribution in the target. We update the proposed objective function with two loss functions. The auxiliary loss guides the generator to create a coarse image, while the coarse-to-fine block next to the generator block produces an image that obeys the distribution of the target domain. The coarse-to-fine block contains two submodules based on the densely connected atrous spatial pyramid pooling, which enriches the details of generated images. We conduct extensive experiments on photorealistic stylization and artistic stylization. The experimental results confirm the superiority of the proposed RRUIT.

Random Reconstructed Unpaired Image-to-Image Translation

In this paper, we proposed an image inpainting algorithm, including an interpolation step and a non-local tensor completion step based on a weighted tensor nuclear norm. Specifically, the proposed algorithm adopts the triangular based linear interpolation algorithm firstly to preform the observation image. Second, we extract the non-local similar patches in the image using the patch match algorithm and rearrange them to a similar tensor. Then, we use the tensor completion algorithm based on the weighted tensor nuclear norm to recover the similar tensors. Finally, we regroup all these recovered tensors to obtain the final recovered image. From the image inpainting experiments on color RGB images, we can see that the performance of the proposed algorithm on the peak signal-to-noise ratio (PSNR) and the relative standard error (RSE) are significantly better than other image inpainting methods.

/pdf/a-non-local-tensor-completion-algorithm-based-on-weighted-23su5059.pdf

A Non-Local Tensor Completion Algorithm Based on Weighted Tensor Nuclear Norm

The purpose of grasp pattern recognition is to determine the grasp type for an object to be grasped, which can be applied to prosthetic hand control and ease the burden of amputees. To enhance the performance of grasp pattern recognition, we propose a network DL-Net inspired by dictionary learning. Our method includes two parts: 1) forward propagation for sparsity representation learning and 2) backward propagation for dictionary learning, which utilizes the sparsity prior effectively and learns a discriminative dictionary with stronger expressive ability from a mass of training data. The experiment was performed on two household object datasets, the RGB-D Object dataset, and the Hit-GPRec dataset. The experimental results illustrate that the DL-Net performs better than traditional deep learning methods in grasp pattern recognition.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10015723.pdf

Huiling Chen

Papers

Video Deblurring via Spatiotemporal Pyramid Network and Adversarial Gradient Prior

Random Reconstructed Unpaired Image-to-Image Translation

A Non-Local Tensor Completion Algorithm Based on Weighted Tensor Nuclear Norm

DL-Net: Sparsity Prior Learning for Grasp Pattern Recognition