Vehicle logistics intermodal route optimization based on Tabu search algorithm.

With the development of logistics enterprises and the adjustment of some relevant laws and regulations, the profit space of vehicle logistics enterprises has been further compressed. To reduce vehicle logistics transportation cost and increase the profit space of vehicle logistics, the vehicle logistics multimodal transport network is constructed and the graph traversal algorithm is used to screen the feasible paths in the vehicle logistics multimodal transport network. Then, the Tabu search algorithm can optimize vehicle logistics multimodal transport route model. Results showed that Tabu search performed better than other methods in solving route optimization problems. The cost of Tabu search algorithm after convergence was 1.2 yuan/km × per set. The performance of Tabu search algorithm on NGSIM data set was better than other methods. On this data set, the area under the curve of Tabu search algorithm was much higher than that of other methods. The optimization results of Tabu search for vehicle logistics intermodal routes were effective. Among the 15 routes, only four routes were not optimized, and other routes were optimized. After optimization, the profits have increased, and the profit of Route 9 had the largest increase, which was 18%. The research successfully constructs the optimization model of vehicle logistics intermodal route, and completes the solution to increase the profit space of vehicle logistics enterprises.

A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm.

Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.

A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area.

This paper presents a scheduling problem of using multiple synthetic aperture radar (SAR) satellites to observe a large irregular area (SMA). SMA is usually considered as a kind of nonlinear combinatorial optimized problem and its solution space strongly coupled with geometry grows exponentially with the increasing magnitude of SMA. It is assumed that each solution of SMA yields a profit associated with the acquired portion of the target area, and the objective of this paper is to find the optimal solution yielding the maximal profit. The SMA is solved by means of a new method composed of three successive phases, namely, grid space construction, candidate strip generation and strip selection. First, the grid space construction is proposed to discretize the irregular area into a set of points in a specific plane rectangular coordinate system and calculate the total profit of a solution of SMA. Then, the candidate strip generation is designed to produce numerous candidate strips based on the grid space of the first phase. At last, in the strip selection, the optimal schedule for all the SAR satellites is developed based on the result of the candidate strip generation. In addition, this paper proposes a normalized grid space construction algorithm, a candidate strip generation algorithm and a tabu search algorithm with variable neighborhoods for the three successive phases, respectively. To verify the effectiveness of the proposed method in this paper, we perform simulation experiments on several scenarios and compare our method with the other seven methods. Compared to the best of the other seven methods, our proposed method can improve profit by 6.38% using the same resources.

Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms.

At present, the COVID-19 epidemic is still spreading at home and abroad, and the foreign exchange market is highly volatile. From financial institutions to individual investors, foreign exchange asset allocation has become important contents worthy of attention. However, most intelligent optimization algorithms (hereinafter IOAS) adopt the existing data and ignore the forecasted one in the foreign exchange portfolio allocation, which will result in a huge difference between portfolio allocation and actual demand; at the same time, many IOAS are less adaptable and have lower optimization ability in portfolio problems. To solve the aforementioned problems, this paper first proposed a DETS based on hybrid tabu search and differential evolution algorithms (DEAs), which has excellent optimization ability. Subsequently, the DETS algorithm was applied to support vector machine (SVM) model. Experiments show that, compared with other algorithms, the MAE and RMSE obtained by using DETS optimization parameters are reduced by at least 3.79 and 1.47%, while the CTR is improved by at least 2.19%. Then combined with the DETS algorithm and Pareto sorting theory, an algorithm suitable for multi-objective optimization was further proposed, named NSDE-TS. Finally, by applying NSDE-TS algorithm, the optimal foreign exchange portfolio is acquired. The empirical analysis shows that the Pareto front obtained by this algorithm is better than that of NSGA-II. Since the lower the uniformity index and convergence index, the stronger the optimization performance of the corresponding algorithm, compared with NSGA-II, its uniformity and convergence index decreased by 15.7 and 39.6%.

Complex Network Analysis of Xiyanping Injection in Treatment of Bronchitis Based on Real World / 中国实验方剂学杂志

Objective:To explore the group modules of Chinese medicine and western medicine for the treatment of bronchitis patients with Xiyanping injection based on the real world to provide references for the clinical treatment of bronchitis with Chinese medicine and western medicine. Method:Medical records of 13 874 patients with bronchitis treated by Xiyanping injection were extracted from 29 hospital information systems （HISs） in China，and complex network analysis was carried out using Tabu Search algorithm to obtain the substructure and associated information of core drug combination of Xiyanping injection for the treatment of bronchitis and to analyze clinical medication protocols. Result:Medication protocols for the pathogens of bronchitis are listed below： Xiyanping injection + cefuroxime for bacterial infection. Xiyanping injection + interferon for viral infection. Xiyanping injection + azithromycin for mycoplasma infection. According to the clinical symptoms and complications of bronchitis， the appropriate medication protocols were as follows： Xiyanping injection + ambroxol + Feilike mixture + ibuprofen for uncomplicated bronchitis. Xiyanping injection + ipratropium bromide + budesonide + salbutamol for asthmatic bronchitis. Xiyanping injection + mannitol + furosemide + phenobarbital + gangliosides + immunoglobulin for bronchitis complicated with viral encephalitis. Xiyanping injection + creatine phosphate sodium + vitamin C for bronchitis complicated with viral myocarditis. The combined medication of Chinese medicine for the treatment of bronchitis was adopted based on its characteristics of traditional Chinese medicine. Conclusion:The data of this study were derived from the real world. The combined medications protocols of Xiyanping injection targeting the clinical symptoms and complications of bronchitis were extracted and summarized. However， it is necessary to formulate an individualized medication protocol according to the specific condition.

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune-Ant Colony Algorithm.

In response to the emergent public health event of COVID-19, the efficiency of transport of medical waste from hospitals to disposal stations is a worthwhile issue to study. In this paper, based on the actual situation of COVID-19 and environmental impact assessment guidelines, an immune algorithm is used to establish a location model of urban medical waste storage sites. In view of the selection of temporary storage stations and realistic transportation demand, an efficiency-of-transport model of medical waste between hospitals and temporary storage stations is established by using an ant colony-tabu hybrid algorithm. In order to specify such status, Wuhan city in Hubei Province, China-considered the first city to suffer from COVID-19-was chosen as an example of verification; the two-level model and the immune algorithm-ant colony optimization-tabu search (IA-ACO-TS) algorithm were used for simulation and testing, which achieved good verification. To a certain extent, the model and the algorithm are proposed to solve the problem of medical waste disposal, based on transit temporary storage stations, which we are convinced will have far-reaching significance for China and other countries to dispatch medical waste in response to such public health emergencies.

[Tabu search algorithm analysis on effect of Xingnaojing Injection in treatment of craniocerebral injury and complications in real world].

In this study, Tabu search algorithm was used to analyze the drug combination of Xingnaojing Injection in the treatment of brain injury and complications in the real world, and the clinical drug combination of Xingnaojing Injection in the treatment of brain injury and complications was selected and summarized. The combination of Xingnaojing Injection, Namefen and Citicoline were recommended in the treatment of brain injury and complications. For brain edema and nerve injury, Mannitol/Glycerol Fructose/Furosemide+Tetrahexose Monosialate Ganglioside Sodium should be recommended. For those with pulmonary infection, Xingnaojing Injection+Xiyanping+Ambroxol Hydrochloride+Tanreqing Injection should be recommended. For those with shock, Hydroxyethyl Starch+Dopamine Hydrochloride/Dobutamine Hydrochloride+Sodium Bicarbonate should be recommended. The combination reflected the characteristics of Chinese and Western medicine. The above medication regimen was only for clinicians' reference. The clinical application should be based on patients' specific conditions, clinical benefits and risks, as well as the incompatibility. On the basis of the findings, further studies can be carried out for prospective clinical efficacy evaluation and safety evaluation for a specific subgroup module.

[Effect of Xingnaojing Injection in treatment of cerebral hemorrhage based on Tabu search algorithm:a real world study].

In this study, Tabu search algorithm was used to analyze the effect of Xingnaojing Injection in the treatment of cerebral hemorrhage in the real world. Through the analysis of the results, the therapies based on the pathogeny of cerebral hemorrhage were screened out: Xingnaojing Injection+hemostatic drugs for promoting blood circulation and removing stasis. Cerebral hemorrhage complicated with brain edema: combined with mannitol or mannitol+aescin. The patients with relevant complications in the acute stage of cerebral hemorrhage could select according to the indications: ①Aminocycline+Oxiracetam+Piperacillin Sodium Sulbactam Sodium+Sodium Lactate Ringer; ②Aminocycline+Oxiracetam+Nifedipine+Captopril+Metoclopramide+Cimetidine; ③Insulin+Pantoprazole+So-dium Nitroprusside. The combined therapies for patients of the stable stage with complicating diseases could select according to the indications: ① Monosialotetrahexosyl Ganglioside Sodium+Deproteinized Calf Blood Serum+Nitroglycerin+Compound Potassium Dihydrogn Phosphate; ② Edaravone+Gangliosides+Captopril+Levofloxacin+Tanreqing Injection+Aminophylline. The analysis of subgroup module of drugs for promoting blood circulation and removing blood stasis suggested that the safety of traditional Chinese medicine should be paid attention to in the treatment of cerebral hemorrhage. This study was based on the data of the real world, but with some problems, such as lack of data and confounding factors. The summarized medication plan is only for the reference of clinicians. The clinical application shall be based on the specific situation of patients and the clinical benefits and risks, and pay attention to the incompatibility.

Tabu search algorithm analysis on effect of Xingnaojing Injection in treatment of craniocerebral injury and complications in real world / 中国中药杂志

In this study, Tabu search algorithm was used to analyze the drug combination of Xingnaojing Injection in the treatment of brain injury and complications in the real world, and the clinical drug combination of Xingnaojing Injection in the treatment of brain injury and complications was selected and summarized. The combination of Xingnaojing Injection, Namefen and Citicoline were recommended in the treatment of brain injury and complications. For brain edema and nerve injury, Mannitol/Glycerol Fructose/Furosemide+Tetrahexose Monosialate Ganglioside Sodium should be recommended. For those with pulmonary infection, Xingnaojing Injection+Xiyanping+Ambroxol Hydrochloride+Tanreqing Injection should be recommended. For those with shock, Hydroxyethyl Starch+Dopamine Hydrochloride/Dobutamine Hydrochloride+Sodium Bicarbonate should be recommended. The combination reflected the characteristics of Chinese and Western medicine. The above medication regimen was only for clinicians' reference. The clinical application should be based on patients' specific conditions, clinical benefits and risks, as well as the incompatibility. On the basis of the findings, further studies can be carried out for prospective clinical efficacy evaluation and safety evaluation for a specific subgroup module.

Effect of Xingnaojing Injection in treatment of cerebral hemorrhage based on Tabu search algorithm:a real world study / 中国中药杂志

In this study, Tabu search algorithm was used to analyze the effect of Xingnaojing Injection in the treatment of cerebral hemorrhage in the real world. Through the analysis of the results, the therapies based on the pathogeny of cerebral hemorrhage were screened out: Xingnaojing Injection+hemostatic drugs for promoting blood circulation and removing stasis. Cerebral hemorrhage complicated with brain edema: combined with mannitol or mannitol+aescin. The patients with relevant complications in the acute stage of cerebral hemorrhage could select according to the indications: ①Aminocycline+Oxiracetam+Piperacillin Sodium Sulbactam Sodium+Sodium Lactate Ringer; ②Aminocycline+Oxiracetam+Nifedipine+Captopril+Metoclopramide+Cimetidine; ③Insulin+Pantoprazole+So-dium Nitroprusside. The combined therapies for patients of the stable stage with complicating diseases could select according to the indications: ① Monosialotetrahexosyl Ganglioside Sodium+Deproteinized Calf Blood Serum+Nitroglycerin+Compound Potassium Dihydrogn Phosphate; ② Edaravone+Gangliosides+Captopril+Levofloxacin+Tanreqing Injection+Aminophylline. The analysis of subgroup module of drugs for promoting blood circulation and removing blood stasis suggested that the safety of traditional Chinese medicine should be paid attention to in the treatment of cerebral hemorrhage. This study was based on the data of the real world, but with some problems, such as lack of data and confounding factors. The summarized medication plan is only for the reference of clinicians. The clinical application shall be based on the specific situation of patients and the clinical benefits and risks, and pay attention to the incompatibility.

Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation.

Multi-objective future rule curves are imperative recommendations for operating multipurpose reservoirs throughout long term periods. This research utilized the conditional tabu search algorithm (CTSA) and conditional genetic algorithm (CGA) combining to the reservoir simulation model through contemplating the multiple-purpose functionals when exploring processes for finding adaptable rule curves of a single reservoir. The historic inflow data during 1966-2016 (51 years) including the future inflow during 2017-2041 (25 years) in case of the B2 scenario of IPCC for the Ubolrat Reservoir in Thailand were applying to create the searching conditions. The 500 synthetic events of historical inflow and 25 years of future inflow were used to calculate the reservoir operation process for assessing the obtained rule curves. As a result, the predicament of water scarcity and spill water were illustrated in terms of frequency scale and duration along with the maintained water at the edge of the rainy period. The operation outcomes suggest that the multi-objective rule curves developed by the CGA can alleviate the frequency of flooding and drought situations appropriately than the CTSA during the future period. However, the rule curves obtained from both optimization techniques indicate better performance correlated to the actual rule curves along with having more maintained water at the end of the rainy period (November), which has continued benefits betwixt the dry period because the reservoir can discharge water in sufficient quantities to the downstream area.

Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response.

Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan.

Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading.

In order to cut the costs of third-party logistics companies and respond to the Chinese government's low-carbon economy plans, this paper studies the more practical and complex open vehicle routing problem, which considers low-carbon trading policies. A low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW) model is constructed with minimum total costs, which include the driver's salary, penalty costs, fuel costs and carbon emissions trading costs. Then, a two-phase algorithm is proposed to handle the model. In the first phase, the initial local solution is obtained with particle swarm optimization (PSO); in the second phase, we can obtain a global optimal solution through a further tabu search (TS). Experiments proved that the proposed algorithm is more suitable for small-scale cases. Furthermore, a series of experiments with different values of carbon prices and carbon quotas are conducted. The results of the study indicate that, as carbon trading prices and carbon quotas change, total costs, carbon emission trading costs and carbon emissions are affected accordingly. Based on these academic results, this paper presents some effective proposals for the government's carbon trading policy-making and also for logistics companies to have better route planning under carbon emission constraints.

Optimal fractional order PID design via Tabu Search based algorithm.

This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional-Order Proportional-Integral-Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method.

Using the Tabu-search-algorithm-based Bayesian network to analyze the risk factors of coronary heart diseases / 中华流行病学杂志

Under the available data gathered from a coronary study questionnaires with 10 792 cases,this article constructs a Bayesian network model based on the tabu search algorithm and calculates the conditional probability of each node,using the Maximum-likelihood.Pros and cons of the Bayesian network model are evaluated to compare against the logistic regression model in the analysis of coronary factors.Applicability of this network model in clinical study is also investigated.Results show that Bayesian network model can reveal the complex correlations among influencing factors on the coronary and the relationship with coronary heart diseases.Bayesian network model seems promising and more practical than the logistic regression model in analyzing the influencing factors of coronary heart disease.

Using the Tabu-search-algorithm-based Bayesian network to analyze the risk factors of coronary heart diseases / 中华流行病学杂志

Under the available data gathered from a coronary study questionnaires with 10 792 cases,this article constructs a Bayesian network model based on the tabu search algorithm and calculates the conditional probability of each node,using the Maximum-likelihood.Pros and cons of the Bayesian network model are evaluated to compare against the logistic regression model in the analysis of coronary factors.Applicability of this network model in clinical study is also investigated.Results show that Bayesian network model can reveal the complex correlations among influencing factors on the coronary and the relationship with coronary heart diseases.Bayesian network model seems promising and more practical than the logistic regression model in analyzing the influencing factors of coronary heart disease.

Heuristic-based tabu search algorithm for folding two-dimensional AB off-lattice model proteins.

The protein structure prediction problem is a classical NP hard problem in bioinformatics. The lack of an effective global optimization method is the key obstacle in solving this problem. As one of the global optimization algorithms, tabu search (TS) algorithm has been successfully applied in many optimization problems. We define the new neighborhood conformation, tabu object and acceptance criteria of current conformation based on the original TS algorithm and put forward an improved TS algorithm. By integrating the heuristic initialization mechanism, the heuristic conformation updating mechanism, and the gradient method into the improved TS algorithm, a heuristic-based tabu search (HTS) algorithm is presented for predicting the two-dimensional (2D) protein folding structure in AB off-lattice model which consists of hydrophobic (A) and hydrophilic (B) monomers. The tabu search minimization leads to the basins of local minima, near which a local search mechanism is then proposed to further search for lower-energy conformations. To test the performance of the proposed algorithm, experiments are performed on four Fibonacci sequences and two real protein sequences. The experimental results show that the proposed algorithm has found the lowest-energy conformations so far for three shorter Fibonacci sequences and renewed the results for the longest one, as well as two real protein sequences, demonstrating that the HTS algorithm is quite promising in finding the ground states for AB off-lattice model proteins.