Multi-objective Harris Hawks Optimization for Optimal Emergency Vehicle Dispatching During a Pandemic

Harris Hawks Optimization (HHO) is a Swarm Intelligence (SI) algorithm that is inspired by the cooperative behavior and hunting style of Harris Hawks in the nature. Researchers' interest in HHO is increasing day by day because it has global search capability, fast convergence speed and strong robustness. On the other hand, Emergency Vehicle Dispatching (EVD) is a complex task that requires exponential time to choose the right emergency vehicles to deploy, especially during pandemics like COVID-19. Therefore, in this work we propose to model the EVD problem as a multi-objective optimization problem where a potential solution is an allocation of patients to ambulances and the objective is to minimize the travelling cost while maximizing early treatment of critical patients. We also propose to use HHO to determine the best allocation within a reasonable amount of time. We evaluate our proposed HHO for EVD using 2 synthetic datasets. We compare the results of the proposed approach with those obtained using a modified version of Particle Swarm Optimization (PSO). The experimental analysis shows that the proposed multi-objective HHO for EVD is very competitive and gives a substantial improvement over the enhanced PSO algorithm in terms of performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.

Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.

A new method for solving the mobile payment scheduling problem using harris hawks optimization algorithm during the COVID-19 pandemic

The Coronavirus Disease 2019 (COVID-19) epidemic is causing once-in-a-century upheavals in global civilization. Payment systems have advanced lately, from simple cash or credit card transactions to various forms of mobile payment systems. This transformation is occurred due to COVID 19 and shifts in the economy, the growth of social networks, technical advancements on the Internet, and the increased usage of mobile devices. Throughout COVID19, this article offers a unique approach to the payment scheduling issue, which seeks out a timetable that enhances the project's stakeholders' benefit. Both the sponsor and the contractor in a project want to have a strong payment plan on their own. To create an equal schedule between the sponsor and the development team, the timing of payments and the completion periods of project activities are decided concurrently. The Harris hawks optimization method is designed to tackle the problem because of its high NP-hardness. Harris hawks optimization is a novel meta-heuristic nature-inspired optimizer inspired by how Harris hawks hunt food in nature. By comparing the suggested Harris hawks optimization optimizer to existing nature-inspired methods, the efficacy of the suggested Harris hawks optimization optimizer is determined. The Harris hawks optimization algorithm appears to be highly promising based on the statistical findings and comparisons. The MATLAB simulator's simulation findings confirm the algorithm's superiority over earlier efforts regarding energy, cost, delay time, and net value. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19.

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.

Comparative analysis of new meta-heuristic-variants for privacy preservation in wireless mobile adhoc networks for IoT applications

"Mobile Ad Hoc Network (MANET)"is a self-configurable, self-repairing, self-maintaining, highly mobile, decentralized, and independent wireless network, which has the liberty to move from one to another place. Such networks do not have any pre-existing infrastructure. The adoption of a smart environment in MANET requires new protocols to connect the gadgets to the internet. A smart environment with routing protocols should assure the following properties like connectivity among the nodes, "Quality of Service (QoS)", and fairness, both in access points and ad-hoc networks. Combination with the Internet of Things (IoT) and MANET generates a novel MANET-IoT system, which focuses on reducing the implementing costs of the network and providing better mobility for users. The necessity of these integrated networks is increasing in military operations, rescue operations, personal area networks, emergency rooms, and meeting rooms. Routing in MANETs is a not simple job and has projected a huge range of attention from researchers around the world. Thus, the intention of this task is a development of a security protocol in MANET for the IoT platform. For dealing with encryption and decryption strategies to handle MANET and IoT data, a new approach is suggested through the enhanced chaotic map. Here, three improved algorithms are implemented for proposing the optimized key management scheme under a chaotic map, which is the Modified Updating-based Harris Hawks Optimization Algorithm (MU-HHO), Mean Solution-based Averaging Sailfish Optimizer (MS-ASFO), Adaptive Basic Reproduction Rate-based Coronavirus Herd Immunity Optimizer (ABRR-CHIO). In the convergence evaluation, while taking the length of plain text as 40, ABRR-CHIO shows superior performance over other techniques at the 60th iteration, which is 96%, 95%, 93%, 96%, and 80% superior to HHO, SFO, CHIO, SA-SFO, and CHHSO. Finally, the performance evaluation is performed regarding "statistical analysis, convergence analysis, and communication overhead"to reveal the superiority of the designed model.

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning.

Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19) has propagated fast all over the world, affecting the lives of billions of people and increasing the mortality rate worldwide in few months. The golden treatment against the invasive spread of COVID-19 is done by identifying and isolating the infected patients, and as a result, fast diagnosis of COVID-19 is a critical issue. The common laboratory test for confirming the infection of COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, these tests suffer from some problems in time, accuracy, and availability. Chest images have proven to be a powerful tool in the early detection of COVID-19. In the current study, a hybrid learning and optimization approach named CovH2SD is proposed for the COVID-19 detection from the Chest Computed Tomography (CT) images. CovH2SD uses deep learning and pre-trained models to extract the features from the CT images and learn from them. It uses Harris Hawks Optimization (HHO) algorithm to optimize the hyperparameters. Transfer learning is applied using nine pre-trained convolutional neural networks (i.e. ResNet50, ResNet101, VGG16, VGG19, Xception, MobileNetV1, MobileNetV2, DenseNet121, and DenseNet169). Fast Classification Stage (FCS) and Compact Stacking Stage (CSS) are suggested to stack the best models into a single one. Nine experiments are applied and results are reported based on the Loss, Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC) performance metrics. The comparison between combinations is applied using the Weighted Sum Method (WSM). Six experiments report a WSM value above 96.5%. The top WSM and accuracy reported values are 99.31% and 99.33% respectively which are higher than the eleven compared state-of-the-art studies.

Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans.

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT-scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85% on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also, comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms.