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Number of new infected individuals for a 20-bit binary codification execution, with 1, 4, and 8 strains.
Source publication
This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability...
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... experiment has been launched 50 times and, on average, the optimum value was found during the iteration number 13, 6, and 3, for 1, 4, and 8 strains, respectively. Figure 2 illustrates the evolution of the new infected population over time, for 1, 4, and 8 strains. The number of new infected people increases exponentially during the first SOCIAL DISTANCING = 8 iterations because R 0 > 0 but, from iteration 9 onward, an acute decrease is reported because R 0 becomes <0. ...
Context 2
... experiment has been launched 50 times and, on average, the optimum value was found during the iteration number 13, 6, and 3, for 1, 4, and 8 strains, respectively. Figure 2 illustrates the evolution of the new infected population over time, for 1, 4, and 8 strains. The number of new infected people increases exponentially during the first SOCIAL DISTANCING = 8 iterations because R 0 > 0 but, from iteration 9 onward, an acute decrease is reported because R 0 becomes <0. ...
Citations
... The propagation and dispersal stage of the Corona Virus pandemic serves as an in-spiration for it. The steps in the CVOA method are easily explained by method (1) [32]. ...
Human iris’ identification is a constantly developing technology and it has it’s own significant in many commonplace applications such as financial sector, identity verification, evidence analysis, law enforcement, and security standards. Several obstacles face the recognition of the iris and the high variation in its captured image is one the most highly affected that is brought on by many factors including aging, illumination, and occlusion. Furthermore, there are some issues with the computing time and complexity of systems concerned in recognizing iris that require attention. In this research, a proposed Iris recognition system that can show a high recognition accuracy and a reduced time is presented. The Corona Virus Optimization Algorithm is a sophisticated bioinspired algorithm that serves as the foundation for the suggested system. The main objective of the suggested approach is to increase the iris identification accuracy rate by fi-ne-tuning the hyperparameter of six conventional Machine Learning models and selecting as well refining the most useful features. Four versions of Iris Image Database known as of CASIA (i.e., 1.0, 2.0, 3.0, 4.0), have been employed to test the system. The evaluation experiment outcomes findings proven the system’s efficiency in catching the high recognition accuracy in uncontrolled environments when compared to current methods. This is accomplished in a through a recognition time ranging from 1564.16 to 13.97 milliseconds, requiring extraordinarily little processing complexity and effort to attain 94%–100% accuracy.
... The use of Python's typing features simplifies the development process and debugging. • Eight implemented metaheuristics, including CVOA (Coronavirus Optimization Algorithm) [5]: MetaGen provides a robust selection of algorithms, ensuring flexibility in solving various optimization problems. ...
... Additionally, the Domain class provides a standard interface between the metaheuristic and its potential users (Solvers), ensuring that the values of generated or modified solutions fall within the specified range. The metagen.metaheuristics package includes eight developed metaheuristics: Random Search [23], Tabu Search [24], Treestructure Parzen Estimator (TPE) [25], Simulated Annealing [26], Genetic Algorithm [27], Steady State Genetic Algorithm [28], Memetic algorithm [29], and Coronavirus Optimization Algorithm [5]. A Solver defines a problem using the Domain class and solves it with one of these available metaheuristics (or another implemented by a thirdparty Developer). ...
... GAs are founded on Darwin's theory of natural selection, where the optimal population is arrived at through iterative processes involving selection, crossover, and mutation. This category of algorithms is also extensive in scope; The coronavirus optimization algorithm (COA) [12] models the spread of the coronavirus starting from patient zero, taking into account the probability of reinfection and the implementation of social measures. In addition, differential evolutionary algorithms (DEs) [13], the invasive tumor growth optimization algorithm (ITGO) [14], the love evolution algorithm (LEA) [15], and so on belong to the category of EAs. ...
The Kepler optimization algorithm (KOA) is a metaheuristic algorithm based on Kepler’s laws of planetary motion and has demonstrated outstanding performance in multiple test sets and for various optimization issues. However, the KOA is hampered by the limitations of insufficient convergence accuracy, weak global search ability, and slow convergence speed. To address these deficiencies, this paper presents a multi-strategy fusion Kepler optimization algorithm (MKOA). Firstly, the algorithm initializes the population using Good Point Set, enhancing population diversity. Secondly, Dynamic Opposition-Based Learning is applied for population individuals to further improve its global exploration effectiveness. Furthermore, we introduce the Normal Cloud Model to perturb the best solution, improving its convergence rate and accuracy. Finally, a new position-update strategy is introduced to balance local and global search, helping KOA escape local optima. To test the performance of the MKOA, we uses the CEC2017 and CEC2019 test suites for testing. The data indicate that the MKOA has more advantages than other algorithms in terms of practicality and effectiveness. Aiming at the engineering issue, this study selected three classic engineering cases. The results reveal that the MKOA demonstrates strong applicability in engineering practice.
... In [47], the authors proposed the Coronavirus Optimization Algorithm (CVOA) to model virus spread and infection, avoiding arbitrary initialization and iteration termination. However, it faces challenges like exponential growth of infected populations and the absence of a candidate reduction mechanism, affecting performance and convergence. ...
... . The convergence of the COVO algorithm is compared with EHO [35], SSO [40], SSA [44], SFO [42], BOA [41], BWO [43], SMO [32], CVOA [47], SRO [53], and GBRUN [54] respectively. The error in fitness value acquired for the 13 standard benchmark functions is shown in Figure 5. ...
... The fitness function 2 F is lower with COVO, even under higher variation in the count of the fitness evaluations. The error in fitness function 13 F is lower than the existing models like EHO [35], SSO [40], SSA [44], SFO [42], BOA [41], BWO [43], SMO [32], CVOA [47], SRO [53], and GBRUN [54] respectively. The convergence performance of various optimization methods compared to the proposed COVO algorithm shows varying levels of effectiveness. ...
The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are inspired by natural phenomena. Recently, the propagation of the new COVID-19 implied a burden on the public health system to suffer several deaths. Vaccination, masks, and social distancing are the major steps taken to minimize the spread of the deadly COVID-19 virus. Considering the social distance to combat the coronavirus epidemic, a novel bio-inspired metaheuristic optimization model is proposed in this work, and it is termed as Social Distancing Induced Coronavirus Optimization Algorithm (COVO). The pace of propagation of the coronavirus can indeed be slowed by maintaining social distance. Thirteen benchmark functions are used to evaluate the COVO performance for discrete, continuous, and complex problems, and the COVO model performance is compared with other well-known optimization algorithms. The main motive of COVO optimization is to obtain a global solution to various applications by solving complex problems with faster convergence. At last, the validated results depict that the proposed COVO optimization has a reasonable and acceptable performance.
... Particle swarm optimisation is a random algorithm that relies on the intelligent behaviour of individuals and groups of animals, such as flocks of birds or schools of fish, to find optimal solutions [28,29]. The coronavirus algorithm, inspired by the spread of the virus and the probability of infection, uses terms like infected person, healthy person, distancing, death, and complete recovery [23,30]. ...
With the heavy reliance on computers and information technology to send and receive data across networks of various types, there has been concern about securing that data from intrusions and cyber-attacks. The expansion of network usage has led to an increase in hacker attacks, which has led to prioritizing cybersecurity precautions in detecting potential threats. Intrusion detection techniques are a critical security measure to protect networks in both personal and corporate environments that are managed by network operating systems. For this, the paper relies on designing a network intrusion detection model. Since deep neural networks (DNNs) are classic deep learning models known for their strong classification performance, making them popular in intrusion detection along with other machine learning algorithms, they have been chosen to improve intrusion classification models based on datasets for intrusion detection systems. The basic structure of this proposal is to adopt one of the optimization algorithms in extracting features from the dataset to obtain more accurate results in the classification and intrusion detection stage. The developed Corona Virus algorithm is adopted to improve the system performance by identifying optimal features. This algorithm, which consists of several stages, optimally selects individuals based on features from the NSL-KDD dataset used for intrusion detection. The resulting optimization solution acts as a network structure for the intrusion classification model based on machine learning and deep learning algorithms. The test results showed exceptional performance on the NSL-KDD dataset, where the proposed Convolution Neural Network CNN model achieved 99.3% accuracy for multi-class classification, while the Decision Tree (DT) achieved 88.64% accuracy for anomaly detection in bi-class classification.
... Recently, inspired by the pandemic of Covid-19, new metaheuristics were proposed motivated by the power of spreading behavior of the virus. Martínez-Álvarez and coauthors (2020) [27] introduced a Coronavirus Optimization Algorithm (CVOA) based on the concepts of social distancing measures, superspreading rate, reinfection probability, and traveling rate to solve the problem of electricity load time series forecasting. Hosseini and coauthors (2020) [28] presented the Covid-19 Optimizer Algorithm (CVA) to simulate the virus distribution process in several countries around the globe and make decisions by governments and authorities in practicing lockdowns. ...
The problem of minimizing total quadratic completion time in flow shops presents a significant challenge in industries such as chemical, metallurgical, and ceramic manufacturing. Initially investigated by Ren and coauthors (2016) [1], this problem addresses the need to balance intermediate inventory reduction with maximizing resource utilization, particularly in multi-objective scenarios. We proposed two innovative metaheuristics (Covid and CHIO algorithms) and a mathematical optimization model. Evaluations were conducted across two industrial settings and three additional benchmarks from existing literature. Through the statistical analysis and performance profiling, our findings indicate that the Covid and CHIO algorithms outperform the Differential Evolution of Ren and coauthors (2016) [1] and the Iterated Greedy Algorithm of Pan and Ruiz (2012) [2], as well as other techniques. Notably, the proposed Covid and CHIO algorithms achieved an average relative deviation from the optimal solution of 0.41% and 0.23%, respectively. Furthermore, they consistently outperformed other methods, securing the best solution in at least 12.5% of instances across all benchmarks, with their worst solutions closer to the best solutions than those produced by alternative approaches.
... In late 2019, the world was hit by a severe pandemic (Covid-19) which claimed many lives. Study and analysis of the behavior of COVID-19 have inspired a lot of optimization algorithms such as coronavirus optimization algorithm [35], COVID-19-based optimization algorithm (C-19BOA) [36], Coronavirus optimization (CVO) [37], and coronavirus herd immunity optimizer (CHIO) [38]. While these algorithms proved an encouraging execution in treating optimization issues, CHIO may be considered as the most efficient because of its flexible and adaptable control parameters which allow effective investigating and exploring of search areas [39]. ...
Phasor measurement units (PMU) are currently considered as an essential step toward the future smart grid due to their capability in increasing the power system's situation awareness. Due to their high costs and limited resources, optimal placement of PMUs (OPP) is an important challenge to compute the minimum number of PMUs and their optimal distribution in the power systems for achieving full monitoring. The coronavirus herd immunity optimizer (CHIO) is a novel optimization algorithm that emulates the flock immunity strategies for the elimination of the coronavirus pandemic. In this research, the CHIO is adapted for the OPP problem for full fault observability. The proposed algorithm is implemented on power systems considering the zero injection bus impacts. A program is created in MATLAB® environment to implement the proposed algorithm. The algorithm is applied to different test systems including; IEEE 9-bus, 14-bus, 30-bus, 118-bus, 300-bus, New England 39-bus and Polish 2383-bus. The proposed CHIO-based OPP is compared to some exact and metaheuristic-based OPP techniques. Compared to these techniques, the promising results have proved the effectiveness and robustness of the proposed CHIO to solve the OPP problem for full fault observability.
... To tackle these challenges, various metaheuristic algorithms have been proposed and applied in similar contexts [8,9]. These algorithms offer promising solutions by optimizing the trajectory and minimizing the distance traveled, allowing for the effective distribution of vaccines and medications in rural areas. ...
The optimization of the vaccination campaign and medication distribution in rural regions of Morocco conducted by the Ministry of Health can be significantly improved by employing metaheuristic algorithms in conjunction with a tour planning system. This research proposes the utilization of six metaheuristic algorithms: genetic algorithm, rat swarm optimization, whale optimization, spotted hyena optimizer, penguins search optimization, and particle swarm optimization, to determine the most efficient routes for equipped trucks carrying vaccines and medications. These algorithms consider critical field constraints, such as operating hours of vaccination centers, vaccine availability, and distances between centers while minimizing the overall journey duration. In addition, a comprehensive tour planning system is integrated into the optimization framework accounting for transportation costs such as fuel expenses and truck maintenance costs. By incorporating these factors, the Ministry of Health aims to achieve the maximum efficiency while minimizing the financial burden associated with the vaccination campaign in rural areas. The integration of metaheuristics and the tour planning system presents a robust and data-driven solution for the Ministry of Health to enhance the effectiveness of their vaccination and medication distribution campaigns in rural regions of Morocco. This approach not only minimizes costs but also improves overall efficiency by ensuring timely access to vaccines and medications for the rural population. The findings of this research contribute to the growing body of knowledge in the field of healthcare logistics optimization and provide valuable insights for policymakers and practitioners involved in similar campaigns worldwide.
... Evolutionary neural networks (ENNs) are neural network models based on evolutionary computing and neural networks [23]. As a result, in this paper, the concept of evolution is employed to improve the efficiency of hyperparameter estimation, and the coronavirus herd immunity optimizer (CHIO) is used to adjust the hyperparameters [24,25]. CHIO is a metaheuristic algorithm that was proposed in 2020 and inspired by social distance and a population immune strategy. ...
... The linear and nonlinear model prediction result sequences were fused, and the coronavirus herd immunity optimizer (CHIO) was introduced to optimize the model hyperparameters. Then, the optimization results were obtained [24,25]. ...
Background
Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research.
Results
The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575.
Conclusions
We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.
