Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Earthquakes occurred in recent years have highlighted the need to examine the strength of reinforced concrete (RC) members. RC beams are one of the elements of reinforced concrete structures. Due to the dramatic increase in the population and the number of medium/high-rise buildings, in recent years, the beams of buildings have been mainly designed and executed in the type of deep beams. In this study, the artificial neural network (ANN) with optimization algorithms, including particle swarm optimization (PSO), Archimedes optimization algorithm (AOA), and sparrow search algorithm (SSA), are used to determine the shear strength of reinforced concrete deep (RCD) beams. 271 samples from experimental tests are employed to develop algorithms. The results of this study, design codes equations, and previous research are compared. Comparison between the results shows that the PSO-ANN algorithm is more accurate than previous methods. Finally, SHApley Additive exPlanations (SHAP) method is utilized to explain the predictions. SHAP reveals that the beam span and the ratio of the beam span to beam depth have the highest impact in predicting shear strength

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

[EN] The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).García, J.; Yepes, V.; Martí Albiñana, JV. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics. 8(4):1-22. https://doi.org/10.3390/math8040555S12284García, J., Altimiras, F., Peña, A., Astorga, G., & Peredo, O. (2018). A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. Complexity, 2018, 1-15. doi:10.1155/2018/8395193García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational Intelligence and Neuroscience, 2019, 1-16. doi:10.1155/2019/3238574Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 10(12), 3445-3465. doi:10.1007/s13042-019-00931-8Kim, M., & Chae, J. (2019). Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path. Mathematics, 7(2), 154. doi:10.3390/math7020154García, J., Crawford, B., Soto, R., & Astorga, G. (2019). A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics. Swarm and Evolutionary Computation, 44, 646-664. doi:10.1016/j.swevo.2018.08.006García, J., Lalla-Ruiz, E., Voß, S., & Droguett, E. L. (2020). Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics, 11(9), 1951-1970. doi:10.1007/s13042-020-01085-8García, J., Moraga, P., Valenzuela, M., & Pinto, H. (2020). A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem. Mathematics, 8(4), 507. doi:10.3390/math8040507Saeheaw, T., & Charoenchai, N. (2018). A comparative study among different parallel hybrid artificial intelligent approaches to solve the capacitated vehicle routing problem. International Journal of Bio-Inspired Computation, 11(3), 171. doi:10.1504/ijbic.2018.091704Valdez, F., Castillo, O., Jain, A., & Jana, D. K. (2019). Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications. Computational Intelligence and Neuroscience, 2019, 1-2. doi:10.1155/2019/9128451Penadés-Plà, V., García-Segura, T., & Yepes, V. (2020). Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge. Mathematics, 8(3), 398. doi:10.3390/math8030398García-Segura, T., Yepes, V., Frangopol, D. M., & Yang, D. Y. (2017). Lifetime reliability-based optimization of post-tensioned box-girder bridges. Engineering Structures, 145, 381-391. doi:10.1016/j.engstruct.2017.05.013Yepes, V., Martí, J. V., & García, J. (2020). Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls. Sustainability, 12(7), 2767. doi:10.3390/su12072767Marti-Vargas, J. R., Ferri, F. J., & Yepes, V. (2013). Prediction of the transfer length of prestressing strands with neural networks. Computers and Concrete, 12(2), 187-209. doi:10.12989/cac.2013.12.2.187Fu, W., Tan, J., Zhang, X., Chen, T., & Wang, K. (2019). Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery. Complexity, 2019, 1-17. doi:10.1155/2019/3264969Sierra, L. A., Yepes, V., García-Segura, T., & Pellicer, E. (2018). Bayesian network method for decision-making about the social sustainability of infrastructure projects. Journal of Cleaner Production, 176, 521-534. doi:10.1016/j.jclepro.2017.12.140Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., & Paredes, F. (2017). Putting Continuous Metaheuristics to Work in Binary Search Spaces. Complexity, 2017, 1-19. doi:10.1155/2017/8404231Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222, 175-184. doi:10.1016/j.ins.2012.08.023Pan, W.-T. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. doi:10.1016/j.knosys.2011.07.001Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:10.1016/j.ins.2009.03.004Calvet, L., Armas, J. de, Masip, D., & Juan, A. A. (2017). Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Mathematics, 15(1), 261-280. doi:10.1515/math-2017-0029Talbi, E.-G. (2015). Combining metaheuristics with mathematical programming, constraint programming and machine learning. Annals of Operations Research, 240(1), 171-215. doi:10.1007/s10479-015-2034-yJuan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62-72. doi:10.1016/j.orp.2015.03.001Chou, J.-S., & Nguyen, T.-K. (2018). Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression. IEEE Transactions on Industrial Informatics, 14(7), 3132-3142. doi:10.1109/tii.2018.2794389Sayed, G. I., Tharwat, A., & Hassanien, A. E. (2018). Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Applied Intelligence, 49(1), 188-205. doi:10.1007/s10489-018-1261-8De León, A. D., Lalla-Ruiz, E., Melián-Batista, B., & Marcos Moreno-Vega, J. (2017). A Machine Learning-based system for berth scheduling at bulk terminals. Expert Systems with Applications, 87, 170-182. doi:10.1016/j.eswa.2017.06.010García, J., Crawford, B., Soto, R., Castro, C., & Paredes, F. (2017). A k-means binarization framework applied to multidimensional knapsack problem. Applied Intelligence, 48(2), 357-380. doi:10.1007/s10489-017-0972-6Molina-Moreno, F., Martí, J. V., & Yepes, V. (2017). Carbon embodied optimization for buttressed earth-retaining walls: Implications for low-carbon conceptual designs. Journal of Cleaner Production, 164, 872-884. doi:10.1016/j.jclepro.2017.06.246Asta, S., Özcan, E., & Curtois, T. (2016). A tensor based hyper-heuristic for nurse rostering. Knowledge-Based Systems, 98, 185-199. doi:10.1016/j.knosys.2016.01.031Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A. A., & Burke, E. K. (2016). A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254(1), 169-178. doi:10.1016/j.ejor.2016.02.045Ghazali, R., Deris, M. M., Nawi, N. M., & Abawajy, J. H. (Eds.). (2018). Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing. doi:10.1007/978-3-319-72550-5Veček, N., Mernik, M., Filipič, B., & Črepinšek, M. (2016). Parameter tuning with Chess Rating System (CRS-Tuning) for meta-heuristic algorithms. Information Sciences, 372, 446-469. doi:10.1016/j.ins.2016.08.066Ries, J., & Beullens, P. (2015). A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction. Journal of the Operational Research Society, 66(5), 782-793. doi:10.1057/jors.2014.46Yalcinoz, T., & Altun, H. (2001). Power economic dispatch using a hybrid genetic algorithm. IEEE Power Engineering Review, 21(3), 59-60. doi:10.1109/39.911360Kaur, H., Virmani, J., Kriti, & Thakur, S. (2019). A genetic algorithm-based metaheuristic approach to customize a computer-aided classification system for enhanced screen film mammograms. U-Healthcare Monitoring Systems, 217-259. doi:10.1016/b978-0-12-815370-3.00010-4Faris, H., Hassonah, M. A., Al-Zoubi, A. M., Mirjalili, S., & Aljarah, I. (2017). A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Computing and Applications, 30(8), 2355-2369. doi:10.1007/s00521-016-2818-2Faris, H., Aljarah, I., & Mirjalili, S. (2017). Improved monarch butterfly optimization for unconstrained global search and neural network training. Applied Intelligence, 48(2), 445-464. doi:10.1007/s10489-017-0967-3Chou, J.-S., & Thedja, J. P. P. (2016). Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Automation in Construction, 68, 65-80. doi:10.1016/j.autcon.2016.03.015Pham, A.-D., Hoang, N.-D., & Nguyen, Q.-T. (2016). Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. Journal of Computing in Civil Engineering, 30(3), 06015002. doi:10.1061/(asce)cp.1943-5487.0000506Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. Expert Systems with Applications, 44, 320-331. doi:10.1016/j.eswa.2015.09.029Chou, J.-S., & Pham, A.-D. (2017). Nature-inspired metaheuristic optimization in least squares support vector regression for obtaining bridge scour information. Information Sciences, 399, 64-80. doi:10.1016/j.ins.2017.02.051Kuo, R. J., Lin, T. C., Zulvia, F. E., & Tsai, C. Y. (2018). A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis. Applied Soft Computing, 67, 299-308. doi:10.1016/j.asoc.2018.02.039Singh Mann, P., & Singh, S. (2017). Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks. Journal of Network and Computer Applications, 83, 40-52. doi:10.1016/j.jnca.2017.01.031Rosa, R. de A., Machado, A. M., Ribeiro, G. M., & Mauri, G. R. (2016). A mathematical model and a Clustering Search metaheuristic for planning the helicopter transportation of employees to the production platforms of oil and gas. Computers & Industrial Engineering, 101, 303-312. doi:10.1016/j.cie.2016.09.006Faris, H., Mirjalili, S., & Aljarah, I. (2019). Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. International Journal of Machine Learning and Cybernetics, 10(10), 2901-2920. doi:10.1007/s13042-018-00913-2De Rosa, G. H., Papa, J. P., & Yang, X.-S. (2017). Handling dropout probability estimation in convolution neural networks using meta-heuristics. Soft Computing, 22(18), 6147-6156. doi:10.1007/s00500-017-2678-4Rere, L. M. R., Fanany, M. I., & Arymurthy, A. M. (2016). Metaheuristic Algorithms for Convolution Neural Network. Computational Intelligence and Neuroscience, 2016, 1-13. doi:10.1155/2016/1537325Jothi, R., Mohanty, S. K., & Ojha, A. (2017). DK-means: a deterministic K-means clustering algorithm for gene expression analysis. Pattern Analysis and Applications, 22(2), 649-667. doi:10.1007/s10044-017-0673-0García, J., Pope, C., & Altimiras, F. (2017). A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition. Complexity, 2017, 1-14. doi:10.1155/2017/5137317Arunkumar, N., Mohammed, M. A., Abd Ghani, M. K., Ibrahim, D. A., Abdulhay, E., Ramirez-Gonzalez, G., & de Albuquerque, V. H. C. (2018). K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Computing, 23(19), 9083-9096. doi:10.1007/s00500-018-3618-7Abdel-Basset, M., Wang, G.-G., Sangaiah, A. K., & Rushdy, E. (2017). Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimedia Tools and Applications, 78(4), 3861-3884. doi:10.1007/s11042-017-4803-xChi, R., Su, Y., Zhang, D., Chi, X., & Zhang, H. (2017). A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications, 31(S1), 653-670. doi:10.1007/s00521-017-3012-xLi, J., Xiao, D., Lei, H., Zhang, T., & Tian, T. (2020). Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location. Mathematics, 8(2), 149. doi:10.3390/math8020149Pan, J.-S., Song, P.-C., Chu, S.-C., & Peng, Y.-J. (2020). Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub. Mathematics, 8(3), 333. doi:10.3390/math8030333Yepes, V., Alcala, J., Perea, C., & González-Vidosa, F. (2008). A parametric study of optimum earth-retaining walls by simulated annealing. Engineering Structures, 30(3), 821-830. doi:10.1016/j.engstruct.2007.05.023Molina-Moreno, F., García-Segura, T., Martí, J. V., & Yepes, V. (2017). Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms. Engineering Structures, 134, 205-216. doi:10.1016/j.engstruct.2016.12.04

A review paper: optimal test cases for regression testing using artificial intelligent techniques

The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all