Abstract Clustering algorithms play a crucial role in identifying co-expressed genes in microarray data, while feature subset identification is equally important when dealing with large data matrices. In this research paper, we address the problem of simultaneous feature selection and gene expression data clustering within a multi-objective optimization framework. Our approach employs the Archived multi-objective simulated annealing (AMOSA) algorithm to optimize a multi-objective function that incorporates two internal validity indices and a feature weight index. To determine data point membership in different clusters, we utilize a point symmetry-based distance metric. We demonstrate the effectiveness of our proposed approach on three publicly available gene expression datasets using the Silhouette index. Furthermore, we compare the clustering results of our approach, unsupervised feature selection and clustering using Multi-objective optimization framework (UFSC-MOO), to nine other existing techniques, showing its superior performance. Statistical significance is confirmed through Wilcoxon Rank Sum test. Also, biological significance test is employed to show that the obtained clustering solutions are biologically enriched. coexpressed co expressed matrices paper multiobjective multi objective AMOSA (AMOSA index clusters symmetrybased symmetry based metric Furthermore Multiobjective Multi UFSCMOO, UFSCMOO UFSC MOO , (UFSC-MOO) techniques performance Also enriched (UFSC-MOO

Reconstruction of random heterogeneous media has been an increasingly popular theme in materials science, as such media is primarily found in nature and manufactured materials. An ideal reconstruction includes every microstructural feature of the reference image and allows simulations of physical quantities that agree with experimental data. In this work, three 2D reconstructions and three 3D reconstructions are produced. All six are based on a single planar section. Its stereological and metallographic features are compared to those of the reference. 2D and 3D reconstructions from single planar sections were carried out with simulated annealing and a sampling method. The first reconstruction used the co-occurrence correlation function (CCF) and orthogonal sampling. The second employed the two-point correlation function ( S 2) and the lattice point algorithm (LPA) as the sampling method. Finally, the third reconstruction used the S 2 together with the two-point cluster function ( C 2) and the LPA as the sampling method. One of two sampling techniques (orthogonal and LPA) were used. The reconstructions that were done using S 2 and C 2 and LPA sampling provided the best results both in 2D and 3D cases, combining realistic morphology and good compatibility with the reference stereological measures. science data work D produced section method cooccurrence co occurrence CCF (CCF twopoint (LPA Finally cases measures

ABSTRACT This paper deals with the two-dimensional strip packing problem (2D-SPP) with the order/or multi-drop and vertical stability constraints. The existing exact algorithm that solves this problem is not able to provide optimal solutions on large instances in a reasonable amount of time. Hence, with a view to quickly obtain a physically stable packing of minimum height while satisfying the order constraint, the Biased Random-Key Genetic Algorithm (BRKGA) is combined with Bottom-Left-Fill (BLF) and Open Space (OS) heuristics. Both versions of the algorithm (BRKGA+BLF and BRKGA+OS) retrieved optimal solutions on many benchmark instances, consuming lesser computational time than the exact algorithm. A comparative study was also performed between the BRKGA, Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms on newly generated large instances. Moreover, the effectiveness of the BRKGA has also been checked on the classical 2D-SPP and two-dimensional orthogonal packing problem (2D-OPP) datasets. twodimensional two dimensional 2DSPP DSPP 2D SPP D (2D-SPP orderor or multidrop multi drop constraints Hence constraint RandomKey Random Key (BRKGA BottomLeftFill Bottom Left Fill BLF (BLF OS (OS heuristics BRKGA+BLF BRKGABLF BRKGA+OS BRKGAOS SA (SA PSO (PSO Moreover 2DOPP DOPP OPP (2D-OPP datasets

Abstract: In this paper, a new optimization based framework for hyperspectral image classification problem is proposed. Band selection is a primordial step in supervised/unsupervised hyperspectral image classification. It attempts to select an optimal subset of spectral bands from the entire set of hyperspectral cube. This subset is considered as the relevant informative subset of bands. The advantage of an efficient band selection approach is to reduce the hughes phenomenon by removing irrelevant and redundant bands. In this study, we propose a new objective function for the band selection problem by using Simulated Annealing as an optimization method. The proposed approach is tested on three Hyperspectral Images largely used in the literature. Experimental results show the performance and efficiency of the proposed approach.

Abstract This article presents the SANN-GCE algorithm, a spice simulation driven meta-heuristic to design general discrete analog electronic circuits automatically, both circuit topology and component sizing. We introduce an encoding scheme called Geometric Circuit Evolution (GCE) that works associated with the Simulated Annealing algorithm and uses categorized degrees of freedom, that allows distinct characteristics of a circuit to change with different probabilities according to its type during the circuit evolution. We show through a series of seven active test circuits that SANN-GCE, compared to a benchmark, present a median fitness 15.88 times better, with a median standard deviation 6.72 times lower between runs. The median runtime found was 14.17 times lower. SANNGCE SANN GCE metaheuristic meta heuristic automatically sizing (GCE freedom evolution SANNGCE, GCE, benchmark 1588 15 88 15.8 better 672 6 72 6.7 runs 1417 14 17 14.1 158 1 8 15. 67 7 6. 141 14.

ABSTRACT In this paper, we propose a math-heuristic that combines mathematical programming techniques with a heuristic approach based on the Simulated Annealing metaheuristic to solve the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP). This problem arises in the context of a distribution network that is divided in two levels: satellite facilities that connect customers to fulfilment centers where freight originates. As it is an NP-hard problem, the proposed approach combines a cluster-first route-second math-heuristic in which approaches are more appropriate, particularly for problem sizes that are more commonly found in practice. The results of the experiments with benchmark instances show that such cluster-first route-second math-heuristic approach utilizing package solvers (CPLEX and TSP CONCORDE) can effectively help solving the CVRP for small instances when compared to an exact method. The experiments conducted on benchmark instances demonstrated the effectiveness of the proposed “cluster-first, route-second” math-heuristic approach, which utilizes package solvers such as CPLEX and TSP CONCORDE, in solving the CVRP for small instances, outperforming exact methods. This research contributes to demonstrating the potential applications of package solvers on heuristic structures for solving the CVRP. Although the presented math-heuristic has limitations, mainly due to the extensive usage of mathematical programming and the chosen characteristics of the implemented local search operators, it can quickly generate high-quality initial solutions for medium and large instances. By showcasing the “cluster-first, route-second” approach, this paper provides a framework that can be further improved by local search or embedded in other metaheuristics, such as GRASP or tabu search, and has practical implications for various industries. mathheuristic math TwoEchelon Two Echelon 2ECVRP. 2ECVRP ECVRP 2E . E (2E-CVRP) levels originates NPhard NP hard clusterfirst cluster first routesecond route second appropriate practice CONCORDE method clusterfirst, first, “cluster-first methods limitations operators highquality high quality metaheuristics industries (2E-CVRP

Resumen Determinar el espectro de energía de dos haces de rayos X de kilovoltaje (kV), a partir de sus curvas de transmisión, mediante una metodología matemática basada en la transformada de Laplace y el algoritmo de transmisión se asociaron, por medio de una expresión matemática derivada de la solución analítica de la transformada de Laplace. Los datos de transmisión se calcularon relacionando el kerma de aire de los haces atenuados, al pasar por placas de aluminio de diferente espesor, con el de los no atenuados. La función de recocido simulado generalizada, desarrollada en un trabajo anterior, se empleó para descifrar los parámetros de la expresión y así obtener los espectros. La validación se realizó comparando las capas semirreductoras de las curvas de transmisión y las extraídas de los espectros. El error porcentual cuadrático medio entre los datos de transmisión y la curva de ajuste para cada espectro, definido a partir de los parámetros encontrados, fue inferior al 1 %, lo que indica un buen ajuste. Se observó el mismo error cuando se cotejó la primera capa semirreductora de las curvas de transmisión y las de cada espectro reconstruido. El tiempo de cálculo de los parámetros fue de 5 s para 80 kV y 14 s para 120 kV. En ningún caso se obtuvo una solución no realista de los espectros. Estos resultados fueron mejores que los de un trabajo anterior en el que se utilizaron mínimos cuadrados. La metodología de reconstrucción basada en recocido simulado generalizado, empleada en este manuscrito, pudo derivar de manera eficiente los espectros de dos haces de kilovoltaje con una precisión comparable a la de trabajos anteriores. Una limitación es que la validación no se realizó comparando con los espectros del equipo.

Resumo Encontrar o espectro de energia de dois feixes de raios-X de quilovoltagem a partir das curvas de transmissão por meio de uma metodologia matemática baseada na transformada de Laplace e no algoritmo de recozimento simulado generalizado. Os espectros de energia e os dados de transmissão foram associados por meio de uma expressão matemática derivada da solução analítica da transformada de Laplace. Os dados de transmissão foram calculados relacionando o kerma no ar dos feixes atenuados, ao passar por placas de alumínio de diferentes espessuras, com o dos feixes não atenuados. A função de recozimento simulado generalizado, desenvolvida em um trabalho anterior, foi empregada para encontrar os parâmetros da expressão para assim determinar os espectros. A validação da metodologia foi feita pela comparação das camadas semirredutoras obtidas das curvas de transmissão e dos espectros. O erro percentual médio quadrático entre os dados de transmissão e a curva de ajuste, para cada espectro definido a partir dos parâmetros encontrados, foi menor do que 1%, indicando um bom ajuste. O mesmo erro foi observado quando comparada a primeira camada semirredutora das curvas de transmissão e aquelas de cada espectro reconstruído. O tempo de cálculo dos parâmetros foi de 5 s para 80 kV e 14 s para 120 kV. Em nenhum caso, solução não realista dos espectros foi obtida. Os resultados foram melhores do que um trabalho anterior onde mínimos quadrados foram usados. A metodologia de reconstrução fundamentada no recozimento simulado generalizado, empregada neste trabalho, pôde derivar eficientemente os espectros de dois feixes de raios-X com precisão comparável a trabalhos anteriores. Uma limitação é que a validação não foi feita por comparação com os espectros do equipamento.

Abstract (Objective): To unfold the energy spectrum of two kilovoltage (kV) X-ray beams from transmission curves through a mathematical methodology based on Laplace transform and the generalized simulated annealing algorithm. (Methodology) Energy spectra of photon beams and transmission data were associated by means of a mathematical expression derived from the analytical solution of Laplace transform. Transmission data was calculated by relating the air kerma of the attenuated beams, passing through aluminium plates of different thickness, to that of the non-attenuated beam. Generalized simulated annealing function, developed in an early work, was employed to find the parameters of the expression and so determine the spectra. Validation of the methodology was done by the comparison of the half-value layers obtained from transmission curves and the spectra. (Results) The mean square percentage error between transmission data and fitting curve of each spectrum defined from the parameters found was lower than 1% indicating a good adjustment. The same error was observed when the first half-value layer (HVL) from the transmission curves and those of each reconstructed spectrum were compared. Calculation time of parameters was 5 sec for 80 kV and 14 sec for 120 kV. In no case, non-realistic solution of energy spectra was obtained. These results were better than an early work where leastsquares were used. (Conclusion) The reconstruction methodology based on generalized simulated annealing employed in this manuscript can efficiently derive the spectra of two X-ray beams with comparable accuracy to previous work. A limitation is that validation was not done by comparing data with the equipment’s spectra.

The results of dig limit delineation in open pit mining are never truly optimized due to gaps in the underlying data, such as insufficient sampling. Aside from the data uncertainty, there is also an influence on the final dig limit by either humans or by the heuristic character of an optimization method like simulated annealing. Several dig limit optimizers have been published, which can replace the manual dig-limits designing process. However, these dig limit designs are generally not adapted to account for this heuristic character. In this paper we present a stochastic analysis tool that can be used with the results of heuristic dig-limit optimization to increase confidence in the obtained results. First, an enhanced simulated annealing algorithm for dig limit optimization is presented. Then, this algorithm is tested on ten different blasts at the Marigold mine, Nevada, USA, as a case study. Finally, the results are analysed with a destination-based ensemble probability map and an analysis conducted of the final solution data distribution. The generated dig-limit designs of the algorithm include high revenue areas that are excluded in comparable manual designs and show improved objective and revenue values. The analysis tool provides block destination probabilities and box plots with the distribution of opportunity value for the dig limit. Furthermore, with the analysis tool, it is possible to make well-informed design decisions in areas of uncertainty.

Abstract In this work, a BAT Algorithm is proposed to solve the permutation flow shop scheduling problem (PFSSP) with minimizing makespan criterion. In a PFSSP, there are n-jobs and m-machines with a proportional deterioration is considered in which all machines process the jobs in the same order, i.e., a permutation schedule. Every job comprises of a foreordained arrangement of assignment operations, each of which should be handled without intrusion for a given timeframe on a given machine. As of late, optimization algorithms such as ant colony optimization (ACO), simulated annealing (SA), artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO) and tabu search (TS) have assumed a significant role in solving PFSSPs. The popular NEH algorithm is considered as the parent algorithm to find the initial solution, and the makespan is minimized in two stages of simulation. The proposed algorithm is tested on 12 flow shop scheduling bench mark problems from OR Library. The proposed algorithm is validated with a well-chosen set of benchmark problems in the literature. Computational results indicate that the proposed bat algorithm is more efficient than the TLBO & HPSO algorithm.

RESUMO Problemas de agendamento são tarefas da rotina operacional presentes nas empresas, que exigem uma solução ótima para apoiar a decisão. No entanto, esses problemas ainda não foram investigados com frequência na ciência florestal. Neste sentido, propôs-se descrever uma formulação matemática para a otimização da silvicultura sob restrição de agendamento da mão de obra/ sequenciamento das atividades (SSRRMM). Buscando o método mais adequado para solucionar este problema combinatório, comparou-se duas estratégias: i) Programação Linear Inteira (PLI) e ii) Simulated annealing (SA). Os principais critérios para avaliar o desempenho das estratégias foram fornecer soluções fáctiveis em tempo de processamento e custo final do projeto aceitáveis. A instância abordada é um problema real contendo 32 talhões e cinco atividades silviculturais programadas dentro de um prazo de 40 dias. Testou-se ainda três funções objetivo, definindo estudos de caso (S) para atender às decisões recorrentes dos gestores, minimizando: S1 - custo do projeto, S2 - makespan e S3 - utilização da mão de obra. Os resultados revelam um modelo robusto para apoiar o planejador florestal em tarefas a nível operacional. O PLI alcançou a solução ótima apenas para a minimização do custo do projeto (S1) devido à demora no tempo de processamento dos outros estudos de caso. Desta forma, destaca-se o SA como um método eficiente para solucionar o SSRRMM, fornecendo soluções satisfatórias em um tempo reduzido. Todas as funções objetivo se ajustaram adequadamente aos seus objetivos propostos. As funções de makespan e de uso da mão de obra aumentaram US$ 1.820,29 (S2) e US$ 2.146,39 (S3) em relação ao S1, respectivamente, para terminar o projeto mais cedo e reduzir a oscilação do uso da mão de obra ao longo dos dias. Diante dessas descobertas, sugere-se aos futuros pesquisadores incorporar outros desafios na tomada de decisão, envolvendo uma formulação multi-objetivo ou métodos para revelar novos insights dentro do manejo e planejamento florestal.

ABSTRACT Scheduling problems are tasks of the operational routine in companies, which demand an optimal solution to support the decision. However, these problems have not been frequently investigated in forestry science. Therefore, it was proposed to describe a mathematical formulation for silviculture optimization under scheduling restriction of the workforce /sequencing of tasks (SSRCMM). Seeking the most suitable method to solve this combinatorial problem, two strategies were compared: i) Integer Linear Programming (ILP) and ii) simulated annealing (SA). The main criteria to assess strategies’ performance were to provide feasible solutions at an acceptable processing time and final project cost. The instance approached is a real problem outlined in 32 stands and five silvicultural tasks scheduled within a 40-day deadline. Three objective functions were also tested, defining case studies (S) to attend to the recurring managers’ decisions by minimizing: S1 – project cost, S2 – makespan, and S3 – workforce usage. The results reveal a robust model to support the forest planner in operational-level tasks. The ILP achieved the optimal solution only for the minimization of the project cost (S1) due to the delay in processing time of the other case studies. Thus, the SA stands out as an efficient method to solve the SSRCMM by providing satisfactory solutions in a reduced time. All the objective functions fitted properly with their proposed goals. The makespan and workforce usage functions increased by US$1,820.29 (S2) and US$2,146.39 (S3) from the S1, respectively, to finish the project earlier and reduce the oscillation of workforce usage over the days. Facing these findings, it is suggested that future researchers incorporate other challenges in decision-making, involving a multi-objective formulation or methods to reveal new insights for forest management and planning.

Argentina, has a solid higher education system through national public universities, mostly with face-to-face offers, has been going through a process of preventive social isolation in the educational field since mid-March 2020. When thinking about access to education and the transit of students during their educational training, and mainly in the section of higher education and professional training, the advent of COVID-19 has established a series of dissonances and / or counterpoints in the practices educational perceived as 'normal' in the context prior to the pandemic by the actors involved; It has highlighted underlying problems that must be addressed in the immediate future and create a solid foundation for the coming stage of general education. The article focuses on a review of 'policies' and 'normal' uses of teaching practices used, analyzed through different theories and mathematical statistical methods, such as Analytic Hierarchy Process (AHP), Montecarlo simulation, Simulated Annealing (SA), among others, which are perceived in counterpoint with the current virtual context. It is intended to postulate how educational processes should be reconverted in order to achieve universality of access in the current and post-pandemic context.

Argentina, poseedora de un sólido sistema de educación superior a través de universidades nacionales públicas, mayormente con ofertas presenciales, se encuentra transitando un proceso de aislamiento social preventivo en el ámbito educativo desde mediados de marzo de 2020. Al pensar en el acceso a la educación y al tránsito de los alumnos durante su formación educativa, y principalmente en el tramo de educación superior y formación profesional, el advenimiento de COVID-19 ha establecido una serie de disonancias y/o contrapuntos en las prácticas educativas percibidas como ´normales´ en el contexto anterior a la pandemia por los actores involucrados; ha resaltado problemáticas subyacentes que deben ser atendibles en lo inmediato y crear cimientos sólidos para la etapa venidera de educación general. El artÃculo se centra en un repaso de ‘polÃticas’ y usos ´normales´ de prácticas docentes utilizadas, analizadas a través de diferentes teorÃas y métodos estadÃsticos matemáticos, tales como Proceso AnalÃtico Jerárquico (AHP), simulación Montecarlo, Recocido Simulado (SA), entre otras, las cuales son percibidas en contrapunto con el contexto actual de virtualidad. Se pretende postular cómo los procesos educativos deben reconvertirse a fin de lograr la universalidad de acceso en el contexto actual y post- pandemia.

Abstract This work presents a novel technique for estimating the prestressing forces in simply supported beams with axial prestress force. The technique is based on the use of generic finite elements for modeling the beam and experimental time-domain response to simultaneously identify axial forces and generic parameters. Parameter updating is accomplished using a Simulated Annealing algorithm implemented for the solution of the prestress force identification problem. The effectiveness of the method was assessed in numerical simulations and was further verified on an experimental prestressed concrete beam. The results show that the inclusion of generic elements allows the identification of the force to be achieved even in the presence of errors in model parameters, thus eliminating the restraints of previous approaches.

Abstract Electron beam radiotherapy is the most widespread treatment modality to deal with superficial cancers. In electron radiotherapy, the energy spectrum is important for electron beam modelling and accurate dose calculation. Since the percentage depth-dose (PDD) is a function of the beam’s energy, the reconstruction of the spectrum from the depth-dose curve represents an inverse problem. Thus, the energy spectrum can be related to the depth-dose by means of an appropriate mathematical model as the Fredholm equation of the first kind. Since the Fredholm equation of the first kind is ill-posed, some regularization method must be used to achieve a useful solution. In this work the Tikhonov regularization function was solved by the generalized simulated annealing optimization method. The accuracy of the reconstruction was verified by the gamma index passing rate criterion applied to the simulated PDD curves for the reconstructed spectra compared to experimental PDD curves. Results show a good coincidence between the experimental and simulated depth-dose curves according to the gamma passing rate better than 95% for 1% dose difference (DD)/1 mm distance to agreement (DTA) criteria. Moreover, the results show improvement from previous works not only in accuracy but also in calculation time. In general, the proposed method can help in the accuracy of dosimetry procedures, treatment planning and quality control in radiotherapy.

ABSTRACT This paper presents the implementation and evaluation of various methods of dimensionality reduction based on artificial intelligence techniques, and addresses one of their complex problems, such as identifying and reducing a representative set of attributes to assist in the improvement of the classification and prediction models. The quest for optimal subsets of attributes for the classification of data sets have the disadvantage that its time complexity. Search procedures were implemented by genetic algorithms, simulated cooling, sequential search and a hybrid between this and genetic algorithms, so to achieve greater robustness and efficiency. It also implemented several measures of association between variable subsets, based on concepts borrowed from classical statistical theory of Shannon Information. In all cases tested the sample space is reduced by more than 65%. The best results are achieved through the Simulated Annealing algorithm using support vector machines classifier. All these search procedures present a polynomial time complexity of order, this demonstrates the practical feasibility and cost of each procedure computing resources deployed.

RESUMEN En el presente trabajo se presentan la implementación y evaluación de varios métodos de reducción de la dimensionalidad basados en técnicas de inteligencia artificial, y aborda uno de sus complejos problemas, como es el identificar y reducir un conjunto representativo de atributos para así contribuir al mejoramiento de los modelos de clasificación y predicción. La búsqueda de subconjuntos óptimos de atributos para la clasificación de conjuntos de datos presenta el inconveniente de su complejidad temporal. Se implementaron procedimientos de búsqueda por algoritmos genéticos, enfriamiento simulado, búsqueda secuencial y una hibridación entre este último y algoritmos genéticos, con tal de alcanzar mayor robustez y eficiencia. Se implementan además varias medidas de asociación entre subconjuntos variables, a partir de conceptos de la estadística clásica o tomadas de la Teoría de la Información de Shannon. En todos los casos experimentados se reduce el espacio muestral en más del 65%. Los mejores resultados se alcanzan con el algoritmo Enfriamiento Simulado, empleando Máquinas de Soporte Vectorial como clasificador. Todos estos procedimientos de búsqueda presentan una complejidad temporal de orden polinomial, esto demuestra la viabilidad práctica en costo y recursos computacionales de cada procedimiento implementado.

Objective: The objective of this work is the application of the vehicle routing problem and its solution with optimization algorithms to the distribution of resources related to the COVID-19 treatment in Camagüey, Cuba, allowing decision making by health managers. Methods: Heterogeneous vehicle routing problems with time windows were used, combined with optimization algorithms to obtain the solution to the distribution of these resources. Main results: It was experimented with a total of 15 models to study the behavior of the algorithms applied to the problem, using the CVRP library, implemented in MATLAB. Three metaheuristics models were implemented: Estimation of Distributions Algorithms, Simulated Annealing and Variable Neighborhood Search. From the modelled problem, it was proceeded to solve the FSMVRPTW problem using EDA, SA and VNS algorithms, the latter being used because it has an open-source implementation in Excel. Conclusions: The studies about the vehicle routing problem have demonstrated its usefulness in different complex situations as pandemic, to optimize the distribution of resources. In COVID-19 times, having an optimal transport organization to distribute medical resources is a necessary tool for the decision-making process of the health managers in Camagüey, these results can be extended to the rest of provinces in Cuba.

Objetivo: El objetivo de este trabajo es la aplicación del modelo de enrutamiento de vehículos y su solución con algoritmos de optimización para la distribución de insumos relacionados con el servicio asistencial a pacientes aquejados de la COVID-19 en Camagüey - Cuba, en función del perfeccionamiento en la toma de decisiones por los directivos. Métodos: Se utilizaron los problemas de enrutamiento de vehículos heterogéneos con ventanas de tiempos, combinado con algoritmos de optimización para obtener la solución a la distribución de estos recursos. Principales resultados: Se experimentaron con un total de 15 modelos para el estudio del comportamiento de los algoritmos aplicados al problema, donde se utilizó la biblioteca CVRP, implementada en Matlab. Se implementaron tres de metaheurísticas: EDA, SA, VNS. A partir de la información modelada se procedió a la solución del problema FSMVRPTW a través de algoritmos EDA y VNS, utilizándose este último por contar con una implementación de código abierto en Excel. Conclusiones: Los estudios acerca del problema de enrutamiento de vehículos han demostrado su utilidad en diferentes situaciones complejas como las pandemias para optimizar la distribución de recursos. En tiempos de COVID-19, contar con una organización del transporte óptima para distribuir los recursos médicos, es una herramienta vital para la toma de decisiones en la provincia Camagüey, extensible a toda Cuba.