An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

[EN] The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056.García, J.; Astorga, G.; Yepes, V. (2021). An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics. Mathematics. 9(3):1-20. https://doi.org/10.3390/math9030225S12093Al-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/s13042-019-00931-8García, 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:10.1155/2019/3238574Guo, H., Liu, B., Cai, D., & Lu, T. (2016). Predicting protein–protein interaction sites using modified support vector machine. International Journal of Machine Learning and Cybernetics, 9(3), 393-398. doi:10.1007/s13042-015-0450-6Korkmaz, S., Babalik, A., & Kiran, M. S. (2017). An artificial algae algorithm for solving binary optimization problems. International Journal of Machine Learning and Cybernetics, 9(7), 1233-1247. doi:10.1007/s13042-017-0772-7García, J., Martí, J. V., & Yepes, V. (2020). The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm. Mathematics, 8(6), 862. doi:10.3390/math8060862Yepes, V., Martí, J. V., & García, J. (2020). Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls. Sustainability, 12(7), 2767. doi:10.3390/su12072767Talbi, 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.2018.2794389Zheng, B., Zhang, J., Yoon, S. W., Lam, S. S., Khasawneh, M., & Poranki, S. (2015). Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Systems with Applications, 42(20), 7110-7120. doi:10.1016/j.eswa.2015.04.066De 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., 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., 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., 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/s10489-017-0972-6Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering, 137, 106040. doi:10.1016/j.cie.2019.106040Zong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:10.1016/j.ins.2009.03.004Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. doi:10.1016/j.cad.2010.12.015Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845. doi:10.1016/j.cnsns.2012.05.010Cuevas, E., & Cienfuegos, M. (2014). A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Systems with Applications, 41(2), 412-425. doi:10.1016/j.eswa.2013.07.067Xu, L., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2008). SATzilla: Portfolio-based Algorithm Selection for SAT. Journal of Artificial Intelligence Research, 32, 565-606. doi:10.1613/jair.2490Smith-Miles, K., & van Hemert, J. (2011). Discovering the suitability of optimisation algorithms by learning from evolved instances. Annals of Mathematics and Artificial Intelligence, 61(2), 87-104. doi:10.1007/s10472-011-9230-5Peña, J. M., Lozano, J. A., & Larrañaga, P. (2005). Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. Evolutionary Computation, 13(1), 43-66. doi:10.1162/1063656053583432Hutter, F., Xu, L., Hoos, H. H., & Leyton-Brown, K. (2014). Algorithm runtime prediction: Methods & evaluation. Artificial Intelligence, 206, 79-111. doi:10.1016/j.artint.2013.10.003Eiben, A. E., & Smit, S. K. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1), 19-31. doi:10.1016/j.swevo.2011.02.001García, J., Yepes, V., & Martí, J. V. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics, 8(4), 555. doi:10.3390/math8040555García, 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/math8040507Poikolainen, I., Neri, F., & Caraffini, F. (2015). Cluster-Based Population Initialization for differential evolution frameworks. Information Sciences, 297, 216-235. doi:10.1016/j.ins.2014.11.026García, J., & Maureira, C. (2021). A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem. Applied Soft Computing, 102, 107077. doi:10.1016/j.asoc.2020.107077Rice, J. R. (1976). The Algorithm Selection Problem. Advances in Computers Volume 15, 65-118. doi:10.1016/s0065-2458(08)60520-3Burke, E. K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Qu, R. (2013). Hyper-heuristics: a survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695-1724. doi:10.1057/jors.2013.71Florez-Lozano, J., Caraffini, F., Parra, C., & Gongora, M. (2020). Cooperative and distributed decision-making in a multi-agent perception system for improvised land mines detection. Information Fusion, 64, 32-49. doi:10.1016/j.inffus.2020.06.009Crawford, 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:10.1155/2017/8404231Mafarja, M., Aljarah, I., Heidari, A. A., Faris, H., Fournier-Viger, P., Li, X., & Mirjalili, S. (2018). Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems, 161, 185-204. doi:10.1016/j.knosys.2018.08.003Feng, Y., An, H., & Gao, X. (2018). The Importance of Transfer Function in Solving Set-Union Knapsack Problem Based on Discrete Moth Search Algorithm. Mathematics, 7(1), 17. doi:10.3390/math7010017Zhang, G. (2010). Quantum-inspired evolutionary algorithms: a survey and empirical study. Journal of Heuristics, 17(3), 303-351. doi:10.1007/s10732-010-9136-0Srikanth, K., Panwar, L. K., Panigrahi, B., Herrera-Viedma, E., Sangaiah, A. K., & Wang, G.-G. (2018). Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem. Computers & Electrical Engineering, 70, 243-260. doi:10.1016/j.compeleceng.2017.07.023Hu, H., Yang, K., Liu, L., Su, L., & Yang, Z. (2019). Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm. Water Resources Management, 33(7), 2357-2379. doi:10.1007/s11269-018-2138-7Gao, Y. J., Zhang, F. M., Zhao, Y., & Li, C. (2019). A novel quantum-inspired binary wolf pack algorithm for difficult knapsack problem. International Journal of Wireless and Mobile Computing, 16(3), 222. doi:10.1504/ijwmc.2019.099861Kumar, Y., Verma, S. K., & Sharma, S. (2020). Quantum-inspired binary gravitational search algorithm to recognize the facial expressions. International Journal of Modern Physics C, 31(10), 2050138. doi:10.1142/s0129183120501387Balas, E., & Padberg, M. W. (1976). Set Partitioning: A survey. SIAM Review, 18(4), 710-760. doi:10.1137/1018115Borneman, J., Chrobak, M., Della Vedova, G., Figueroa, A., & Jiang, T. (2001). Probe selection algorithms with applications in the analysis of microbial communities. Bioinformatics, 17(Suppl 1), S39-S48. doi:10.1093/bioinformatics/17.suppl_1.s39Boros, E., Hammer, P. L., Ibaraki, T., & Kogan, A. (1997). Logical analysis of numerical data. Mathematical Programming, 79(1-3), 163-190. doi:10.1007/bf02614316Balas, E., & Carrera, M. C. (1996). A Dynamic Subgradient-Based Branch-and-Bound Procedure for Set Covering. Operations Research, 44(6), 875-890. doi:10.1287/opre.44.6.875Beasley, J. E. (1987). An algorithm for set covering problem. European Journal of Operational Research, 31(1), 85-93. doi:10.1016/0377-2217(87)90141-xBeasley, J. E. (1990). A lagrangian heuristic for set-covering problems. Naval Research Logistics, 37(1), 151-164. doi:10.1002/1520-6750(199002)37:13.0.co;2-2Beasley, J. ., & Chu, P. . (1996). A genetic algorithm for the set covering problem. European Journal of Operational Research, 94(2), 392-404. doi:10.1016/0377-2217(95)00159-xSoto, R., Crawford, B., Olivares, R., Barraza, J., Figueroa, I., Johnson, F., … Olguín, E. (2017). Solving the non-unicost set covering problem by using cuckoo search and black hole optimization. Natural Computing, 16(2), 213-229. doi:10.1007/s11047-016-9609-

PEMANFAATAN MATERIAL BIODEGRADABLE (BESE-ELEMENT) SEBAGAI MEDIA RESTORASI MANGROVE DI PESISIR NEGERI PASSO, KOTA AMBON

Mangrove ecosystems have potential both ecologically, economically, physically, and chemically. The abundance of this potential causes pressure from anthropogenic activities, which results in the degradation of mangrove ecosystems. Several mangrove ecosystems in the Ambon City area have experienced degradation, so restoration is necessary. The Passo State area has the potential for a dense mangrove ecosystem and can be used as a nursery area to support other ecosystems. Increasing the potential of mangrove ecosystems in the Passo State area can be done through restoration using biodegradable materials (BESE-Elements). Mangrove restoration is carried out through community service activities, a collaboration between the Maritime Center and Wardeen Burg Ecology – The Netherlands. The result of the activity is that mangrove ecosystem restoration is carried out in semi-enclosed areas using 10 BESE-Element. Each BESE-Element was planted with 10 mangrove seedlings consisting of 5 seedlings and 5 propagules. The mangrove species planted were Bruguiera gymnorrhiza, Rhizophora apicutala, and Rhizophora stylosa. Around BESE-Elements planted seedlings and mangrove propagules as a control for mangroves in BESE-Elements

Komposisi Jenis Karang Keras (Scleractinia) di Perairan Pantai Utara Pulau Ambon

Terumbu karang merupakan ekosistem pesisir yang secara ekologi paling produktif dengan keanekaragaman tinggi. Penelitian ini bertujuan untuk mengetahui komposisi jenis karang keras (Scleractinia) di perairan pantai utara Pulau Ambon. Pengambilan data dilakukan dengan menggunakan metode sistematik sampling. Data karang yang diperoleh kemudian diidentifikasi, proses identifikasi dilakukan dengan teknik analisa visual menggunakan beberapa referensi Veron (2000), Veron (1986), Suharsono (2017), Coral Finder edisi ketiga, dan Website Corals of The World (http://www.coralsoftheworld.org) untuk melihat jenis-jenis apa saja yang ada pada lokasi penelitian. Hasil penelitian menunjukkan bahwa berdasarkan komposisi taksa jumlah genus terbanyak dari famili Fungidae, jumlah spesies terbanyak dari genus Porites dan jumlah koloni terbanyak dari spesies Porites lutea

Correlation of some water quality parameters and Pb in sediment to gastropod diversity in Ambon Island Waters

The coastal waters of Ambon Island have quite diverse ecosystems that allow for the presence of various organisms, one of which is gastropods. This study aims to analyze the correlation of some water parameters and Pb in sediment to the diversity of gastropods. The research method was carried out by observing the density, water quality  parameters, and Pb metal in sediments. Water parameters were measured in situ and analyzed in the laboratory. The distribution of gastropods was analyzed through Principal Component Analysis (PCA). At the same time, the correlation analysis was carried out using the Pearson correlation approach using SPSS v.16. The results showed that the types of gastropods with the highest density in the waters of Ambon Island were Terebralia sulcata, Hebra corticata, and Nerita patula. While the species with the lowest density value were Nassarius olivaceus, Polinices didyma, Lunella cinerea, Conus eburneus, Cypraea isabella, Vexillum plicarium, and Columbella scripta. The Shannon-Wienner Diversity Index ranges from 1.253–2.622, and the diversity index ranges from 0.083-0.207. It was included in the low category caused by the disturbance of water pollution and Pb metal in sediments. Meanwhile, the dominance index ranged from 0.098 to 0.511 indicating species dominance at several observation stations. The waters' physical-chemical parameters strongly correlating with gastropod diversity are DO and Pb, with respective correlation values ​​of r = 0.656 and r = -0.785. &nbsp

Portal protein functions akin to a DNA-sensor that couples genome-packaging to icosahedral capsid maturation.

Tailed bacteriophages and herpesviruses assemble infectious particles via an empty precursor capsid (or \u27procapsid\u27) built by multiple copies of coat and scaffolding protein and by one dodecameric portal protein. Genome packaging triggers rearrangement of the coat protein and release of scaffolding protein, resulting in dramatic procapsid lattice expansion. Here, we provide structural evidence that the portal protein of the bacteriophage P22 exists in two distinct dodecameric conformations: an asymmetric assembly in the procapsid (PC-portal) that is competent for high affinity binding to the large terminase packaging protein, and a symmetric ring in the mature virion (MV-portal) that has negligible affinity for the packaging motor. Modelling studies indicate the structure of PC-portal is incompatible with DNA coaxially spooled around the portal vertex, suggesting that newly packaged DNA triggers the switch from PC- to MV-conformation. Thus, we propose the signal for termination of \u27Headful Packaging\u27 is a DNA-dependent symmetrization of portal protein

Monitoraggio topografico e fotogrammetrico della cupola del teatro Massimo

L’articolo riporta i primi risultati di uno studio finalizzato al monitoraggio delle deformazioni della cupola del Teatro Massimo di Palermo provocate dalle dilatazioni termiche, con l’impiego di tecniche topografiche e fotogrammetriche integrate. In particolare, sono state utilizzate due stazioni totali robotizzate e sistemi di fotogrammetria digitale di elevata precisione. Per correlare gli spostamenti dei carrelli sui quali poggia la struttura in acciaio della cupola con i gradienti termici, sono state acquisite immagini termiche diversificate. L’obiettivo principale del lavoro consisteva nel confronto delle due tecniche di rilievo in relazione a misure di deformazione di entità molto ridotta (sub-millimetrica). I risultati ottenuti dimostrano che i carrelli funzionano ancora correttamente. Infatti, per un gradiente termico di 6°C misurato dalla termo camera, entrambe le tecniche topografiche e fotogrammetriche hanno evidenziato spostamenti dell’ordine di 0.8 mm, in accordo con le previsioni del modello deformativo teorico