Finding groups in data: Cluster analysis with ants

Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. As a case study, this paper focus on the behavior of clustering procedures in those new approaches. The proposed algorithm and its modifications are evaluated in a number of well-known benchmark datasets. Empirical results clearly show that ant-based clustering algorithms performs well when compared to another techniques

The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm

[EN] The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10^20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.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).Garcia, J.; Martí Albiñana, JV.; Yepes, V. (2020). The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm. Mathematics. 8(6):862-01-862-22. https://doi.org/10.3390/math8060862S862-01862-228

A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection

The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem

Scientific research trends about metaheuristics in process optimization and case study using the desirability function

This study aimed to identify the research gaps in Metaheuristics, taking into account the publications entered in a database in 2015 and to present a case study of a company in the Sul Fluminense region using the Desirability function. To achieve this goal, applied research of exploratory nature and qualitative approach was carried out, as well as another of quantitative nature. As method and technical procedures were the bibliographical research, some literature review, and an adopted case study respectively. As a contribution of this research, the holistic view of opportunities to carry out new investigations on the theme in question is pointed out. It is noteworthy that the identified study gaps after the research were prioritized and discriminated, highlighting the importance of the viability of metaheuristic algorithms, as well as their benefits for process optimization

Multi-objective engineering shape optimization using differential evolution interfaced to the Nimrod/O tool

This paper presents an enhancement of the Nimrod/O optimization tool by interfacing DEMO, an external multiobjective optimization algorithm. DEMO is a variant of differential evolution – an algorithm that has attained much popularity in the research community, and this work represents the first time that true multiobjective optimizations have been performed with Nimrod/O. A modification to the DEMO code enables multiple objectives to be evaluated concurrently. With Nimrod/O’s support for parallelism, this can reduce the wall-clock time significantly for compute intensive objective function evaluations. We describe the usage and implementation of the interface and present two optimizations. The first is a two objective mathematical function in which the Pareto front is successfully found after only 30 generations. The second test case is the three-objective shape optimization of a rib-reinforced wall bracket using the Finite Element software, Code_Aster. The interfacing of the already successful packages of Nimrod/O and DEMO yields a solution that we believe can benefit a wide community, both industrial and academic

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

BARD: Better Automated Redistricting

BARD is the first (and at time of writing, only) open source software package for general redistricting and redistricting analysis. BARD provides methods to create, display, compare, edit, automatically refine, evaluate, and profile political districting plans. BARD aims to provide a framework for scientific analysis of redistricting plans and to facilitate wider public participation in the creation of new plans. BARD facilitates map creation and refinement through command-line, graphical user interface, and automatic methods. Since redistricting is a computationally complex partitioning problem not amenable to an exact optimization solution, BARD implements a variety of selectable metaheuristics that can be used to refine existing or randomly-generated redistricting plans based on user-determined criteria. Furthermore, BARD supports automated generation of redistricting plans and profiling of plans by assigning different weights to various criteria, such as district compactness or equality of population. This functionality permits exploration of trade-offs among criteria. The intent of a redistricting authority may be explored by examining these trade-offs and inferring which reasonably observable plans were not adopted. Redistricting is a computationally-intensive problem for even modest-sized states. Performance is thus an important consideration in BARD's design and implementation. The program implements performance enhancements such as evaluation caching, explicit memory management, and distributed computing across snow clusters.

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio