ABSTRACT
The design and planning of group tourist itineraries is a current trend. Group planning should be done according to the maximum capacity of the site under current COVID-19 conditions, the transport flow, and the benefits associated with individual preferences. Tourists commonly express the benefits and limitations of travel in vague and imprecise linguistic terms. In this paper, a hybrid algorithm is presented that combines Greedy Randomized Adaptive Search Procedure, Variable Neighborhood Descendent, and Pareto optimality to solve the multi-objective problem of planning sustainable group tourists itineraries under uncertainty. A set of experiments is performed with real-world tourism data from Sucre, Colombia and benchmark instances from the literature to validate the algorithm's performance. The results are compared with optimal solutions obtained by CPLEX and other algorithms from previous works. Our approach demonstrates superior performance to different multi-target algorithms and builds more realistic routes.(c) 2022 Elsevier B.V. All rights reserved.
ABSTRACT
The design and planning of group tourist itineraries is a current trend. Group planning should be done according to the maximum capacity of the site under current COVID-19 conditions, the transport flow, and the benefits associated with individual preferences. Tourists commonly express the benefits and limitations of travel in vague and imprecise linguistic terms. In this paper, a hybrid algorithm is presented that combines Greedy Randomized Adaptive Search Procedure, Variable Neighborhood Descendent, and Pareto optimality to solve the multi-objective problem of planning sustainable group tourists itineraries under uncertainty. A set of experiments is performed with real-world tourism data from Sucre, Colombia and benchmark instances from the literature to validate the algorithm’s performance. The results are compared with optimal solutions obtained by CPLEX and other algorithms from previous works. Our approach demonstrates superior performance to different multi-target algorithms and builds more realistic routes.
ABSTRACT
Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems, where accurate segmentation is vital for perfect disease diagnoses. This paper proposes a multilevel thresholding technique for 2D and 3D medical image segmentation using Otsu and Kapur's entropy methods as fitness functions to determine the optimum threshold values. The proposed algorithm applies the hybridization concept between the recent Coronavirus Optimization Algorithm (COVIDOA) and Harris Hawks Optimization Algorithm (HHOA) to benefit from both algorithms' strengths and overcome their limitations. The improved performance of the proposed algorithm over COVIDOA and HHOA algorithms is demonstrated by solving 5 test problems from IEEE CEC 2019 benchmark problems. Medical image segmentation is tested using two groups of images, including 2D medical images and volumetric (3D) medical images, to demonstrate its superior performance. The utilized test images are from different modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray images. The proposed algorithm is compared with seven well-known metaheuristic algorithms, where the performance is evaluated using four different metrics, including the best fitness values, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation Coefficient (NCC). The experimental results demonstrate the superior performance of the proposed algorithm in terms of convergence to the global optimum and making a good balance between exploration and exploitation properties. Moreover, the quality of the segmented images using the proposed algorithm at different threshold levels is better than the other methods according to PSNR, SSIM, and NCC values. Additionally, the Wilcoxon rank-sum test is conducted to prove the statistical significance of the proposed algorithm.
ABSTRACT
With the invention of the COVID-19 vaccine, shipping and distributing are crucial in controlling the pandemic. In this paper, we build a mean-field variational problem in a spatial domain, which controls the propagation of pandemics by the optimal transportation strategy of vaccine distribution. Here, we integrate the vaccine distribution into the mean-field SIR model designed in Lee W, Liu S, Tembine H, Li W, Osher S (2020) Controlling propagation of epidemics via mean-field games. arXiv preprint arXiv:2006.01249. Numerical examples demonstrate that the proposed model provides practical strategies for vaccine distribution in a spatial domain.
ABSTRACT
Multi-center location of pharmaceutical logistics is the focus of pharmaceutical logistics research, and the dynamic uncertainty of pharmaceutical logistics multi-center location is a difficult point of research. In order to reduce the risk and cost of multi-enterprise, multi-category, large-volume, high-efficiency, and nationwide centralized medicine distribution, this study explores the best solution for planning medicine delivery for the medicine logistics. In this paper, based on the idea of big data, comprehensive consideration is given to uncertainties in center location, medicine type, medicine chemical characteristics, cost of medicine quality control (refrigeration and monitoring costs), delivery timeliness, and other factors. On this basis, a multi-center location- and route-optimization model for a medicine logistics company under dynamic uncertainty is constructed. The accuracy of the algorithm is improved by hybridizing the fuzzy C-means algorithm, sequential quadratic programming algorithm, and variable neighborhood search algorithm to combine the advantages of each. Finally, the model and the algorithm are verified through multi-enterprise, multi-category, high-volume, high-efficiency, and nationwide centralized medicine distribution cases, and various combinations of the three algorithms and several rival algorithms are compared and analyzed. Compared with rival algorithms, this hybrid algorithm has higher accuracy in solving multi-center location path optimization problem under the dynamic uncertainty in pharmaceutical logistics.
ABSTRACT
The development in the methods used in forecasting has made countries compete to take their place in optimization the use of these methods, especially under the spread of the new Covid-19 epidemic, so in this research we applied the Adaptive Neuro Fuzzy Inference System (ANFIS) to data on infection numbers for the Corona pandemic for all Governorates of Iraq, and the time series data is usually collected over time either for equal intervals or for unequal intervals [Ravichandran et al. (2012)]. And the forecasting results indicated that the forecasting data follow the same path as the actual data. The hybrid algorithm in the ANFIS showed good forecasting results through five independent inputs and a dependent variable that represents the number of injuries. © 2021 DAV College. All rights reserved.
ABSTRACT
The iterative Fourier transform algorithm (IFTA) is widely used in various optical communication applications based on liquid crystal on silicon spatial light modulators. However, the traditional iterative method has many disadvantages, such as a poor effect, an inability to select an optimization direction, and the failure to consider zero padding or phase quantization. Moreover, after years of development, the emergence of various variant algorithms also makes it difficult for researchers to choose one. In this paper, a new intelligent hybrid algorithm that combines the IFTA and differential evolution algorithm is proposed in a novel way. The reliability of the proposed algorithm is verified by beam splitting, and the IFTA and symmetrical IFTA algorithms, for comparison, are introduced. The hybrid algorithm improves the defects above while considering the zero padding and phase quantization of a computer-generated hologram, which optimizes the directional optimization in the diffraction efficiency and the fidelity of the output beam and improves the results of these two algorithms. As a result, the engineers’ trouble in the selection of an algorithm has also been reduced.
ABSTRACT
For the optimization problem of the cold-chain emergency materials (CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization (MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis (IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei Province. Research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. Research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.
ABSTRACT
The coronavirus COVID-19 is affecting 196 countries and territories around the world. The number of deaths keep on increasing each day because of COVID-19. According to World Health Organization (WHO), infected COVID-19 is slightly increasing day by day and now reach to 570,000. WHO is prefer to conduct a screening COVID-19 test via online system. A suitable approach especially in string matching based on symptoms is required to produce fast and accurate result during retrieving process. Currently, four latest approaches in string matching have been implemented in string matching;characters-based algorithm, hashing algorithm, suffix automation algorithm and hybrid algorithm. Meanwhile, extensible markup language (XML), JavaScript object notation (JSON), asynchronous JavaScript XML (AJAX) and JQuery tehnology has been used widelfy for data transmission, data storage and data retrieval. This paper proposes a combination of algorithm among hybrid, JSON and JQuery in order to produce a fast and accurate results during COVID-19 screening process. A few experiments have been by comparison performance in term of execution time and memory usage using five different collections of datasets. Based on the experiments, the results show hybrid produce better performance compared to JSON and JQuery. Online screening COVID-19 is hopefully can reduce the number of effected and deaths because of COVID. © 2022 Institute of Advanced Engineering and Science. All rights reserved.