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Microgrids (MGs) based on renewable energies have emerged as a proficient strategy for tackling power quality issues in conventional distribution networks. Nonetheless, MG systems require a suitable control scheme to supply energy optimally towards the electrical grid. This paper presents an innovative framework for designing hybrid Proportional-Resonant (PR) controllers with Linear Quadratic Regulators (LQR), PR+LQR, which merge relevant properties of PR and LQR controllers. This method simultaneously determines the MG control parameters and the current unbalanced factor generated at the distribution network. We select the traditional IEEE 13-bus test feeder network and place two MGs at strategic locations to validate our approach. Moreover, we use the Grey Wolf Optimizer (GWO) to find control parameters through a reliable fitness function that leads to high-performance microgrids. Finally, we conceive several tests to assess the efficacy of GWO for tuning the hybrid controller and compare the resulting data across distinct realistic operation conditions representing power quality events. So, we choose four case studies considering different renewable energy penetration indexes and power factors and evaluate the effects of the MGs over the distribution grid. We also compare the proposed hybrid PR+LQR controller against closely-related alternatives from the literature and validate its robustness and stability through the disk margin approach and the Nyquist criterion. Our numerical simulations prove that hybrid controllers driven by GWO are highly reliable strategies, yielding an average unbalanced current reduction of 30.03%.

The Robotic Mobile Fulfillment System (RMFS) is a method for handling products, in which a Line Follower Robot (LFR) transports products to a human workstation for packing. In this systematic review, we delve into the current state of RMFS research using data sourced from Scopus. After a comprehensive search, we found 264 manuscripts, which we filtered to 76 relevant articles. Our analysis covers several variables, from basic metadata to manuscript impact and specific conditions the authors consider. We discovered that there needs to be more focus on the pod allocation problem, despite its potential, with the majority of the emphasis on LFR displacement. We created a detailed diagram that outlines the essential elements and subproblems associated with RMFS. As the interest in RMFS continues growing, our study provides crucial insights and direction for future research efforts.

It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three well-known algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems.

This work introduces an alternative approach for developing a customized Metaheuristic (MH) tailored for tuning a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within an Automatic Voltage Regulator (AVR) system. Leveraging an Automated Algorithm Design (AAD) methodology, our strategy generates MHs by utilizing a population-based Search Operator (SO) domain, thus minimizing human-induced bias. This approach eliminates the need for manual coding or the daunting task of selecting an optimal algorithm from a vast collection of the current literature. The devised MH consists of two distinct SOs: a dynamic swarm perturbator succeeded by a Metropolis-type selector and a genetic crossover perturbator, followed by another Metropolis-type selector. This MH fine-tunes the FOPID controller’s parameters, aiming to enhance control performance by reducing overshoot, rise time, and settling time. Our research includes a comparative analysis with similar studies, revealing that our tailored MH significantly improves the FOPID controller’s speed by 1.69 times while virtually eliminating overshoot. Plus, we assess the tuned FOPID controller’s resilience against internal disturbances within AVR subsystems. The study also explores two facets of control performance: the impact of fractional orders on conventional PID controller efficiency and the delineating of a confidence region for stable and satisfactory AVR operation. This work’s main contributions are introducing an innovative method for deriving efficient MHs in electrical engineering and control systems and demonstrating the substantial benefits of precise controller tuning, as evidenced by the superior performance of our customized MH compared to existing solutions.

Hyper-heuristics (HHs) stand as a relatively recent approach to solving optimization problems. There are different kinds of HHs. One of them deals with how low-level heuristics must be combined to deliver an improved solution to a set of problem instances. Literature commonly refers to them as selection hyper-heuristics. One of their advantages is that the strengths of each heuristic can be fused into a high-level solver. However, one of their drawbacks is that sometimes this generalization scheme does not suffice. Additionally, it is not easy to reuse these HHs since the model cannot be easily tweaked. So, in this work, we develop a hyper-heuristic model with an additional layer of generalization. The rationale behind it is to preserve the general structure of selecting an adequate solver for a particular situation but to use HHs instead of low-level heuristics. We call this model a Squared Hyper-Heuristic (SHH). To validate our proposal, we pursue a four-stage methodology that covers several testing scenarios. Our data reveal that, under proper conditions, our model can outperform the base HHs. Moreover, it is flexible enough to allow for an increased number of layers so that the complexity of the final model can be tuned. Additionally, different kinds of instances can be used to train each stage of the model, thus setting the groundwork for developing a transfer learning approach for hyper-heuristics.

Hyper-Heuristics (HHs) have proven to be a valuable tool for solving complex problems, such as Combinatorial Optimization Problems (COPs). These solvers have an assorted set of models arising through extensive research from the scientific community. Hence, it is customary that researchers develop their models from scratch, which increases development times. Drafting and testing new ideas become burdensome and time-consuming. In this work, we present MatHH, a Matlab-based framework to allow rapid prototyping of HHs. We summarize the architecture and some examples of their usage. We also discuss some research questions that upcoming research may explore through MatHH.

Although the number of solutions in combinatorial optimization problems (COPs) is finite, some problems grow exponentially and render exact approaches unfeasible. So, approximate methods, such as heuristics, are customary. Each heuristic usually specializes in specific kinds of problems. Hence, other approaches seek to merge their strengths. One of them is selection hyper-heuristics. However, they usually provide scarce information about their sensitivity. Illumination algorithms may fix this issue since they focus on exploration rather than exploitation while preserving the best solutions under different criteria. Still, literature falls short when merging both approaches, representing a knowledge gap. This work tests the feasibility of using an illumination algorithm, MAP-Elites (ME), for tuning a sequence-based selection hyper-heuristic model for Balanced Partition problems. We choose ME since other researchers have successfully applied it to a different COP. So, we may achieve a hyper-heuristic that represents the best combination of heuristics while simultaneously gaining intel on the performance of diverse alternatives. Our approach operates by creating a multi-dimensional map, where each design variable represents the application of a heuristic. Afterward, ME generates mutated sequences and tests them to determine if they represent a better-performing solution. We consider 1500 instances that include easy and hard instances, analyzed under different scenarios to test our approach. We also include limit instances that are neither easy nor hard. Our resulting data support the proposed approach, as it performs toe-to-toe with a synthetic oracle and may even outperform it. This represents an outstanding result, since a brute-force approach is needed to achieve such an oracle. So, merging ME and hyper-heuristics is a path worth pursuing. We also present how each parameter affects the model performance and identify the critical and virtually irrelevant ones. This serves as the groundwork for future works that focus on exploiting the most relevant parameters.

Problem instances are paramount when testing the performance of any learning algorithm. For this reason, it is customary to use widespread problems known as benchmark instances. Nonetheless, these are usually generated disregarding heuristics and their nature. For Job Shop Scheduling problem, researchers have created such instances based on random distributions. This idea may bias conclusions about the algorithm under test since a practitioner can only observe performance from a limited perspective, which may not even reflect real-life situations. However, addressing this issue implies tackling the instance generation problem while considering the nature of the solution approach. Hence, in this work, we propose an instance generator based on the Unified Particle Swarm Optimization algorithm, which can tailor instances to different goals. To validate our approach, we include instances generated to different heuristics and instances tailored to a variety of features. In the first case, we seek to favor or hinder one heuristic whereas doing the opposite for the remaining ones. In the second one, we explore instances with specific feature values. Our data reveal that the proposed approach fulfills the expectations and can effectively deal with different kinds of instances. We analyse the nature of the generated instances and their insights, which can be used to further the study about heuristics and problem features.

A plethora of combinatorial optimization problems can be linked to real-life decision scenarios. Even nowadays, more diverse and complex problems are popping up. One of these problems is the traveling thief problem (TTP), which combines elements from the knapsack and traveling salesperson problems. Hence, it is paramount to keep improving solvers to tackle combinatorial problems. Among recent proposals, hyper-heuristics have proven useful since they seek to combine the strengths of more straightforward solvers. This paper proposes a sequence-based selection hyper-heuristic and assesses its feasibility when solving the TTP. Our proposal can be represented by an array of operators selecting a city or an item. In the first case, the solution moves to a new city and thus advances the tour. In the second one, the thief agent picks an item within the current city and tries to store it in its knapsack. We generate several sets of TTP instances with different parameters to validate our approach and analyze the model’s performance. Our data reveal that the proposed approach outperforms randomly generated sequences. Moreover, our approach finds general sequences that surpass sequences specialized for each instance. We believe this is noteworthy and represents a stepping stone towards achieving a more robust solver for complex problems.

This work proposes a fractional-order mathematical model of a Buck-Boost converter performing in continuous conduction mode. To do so, we employ the average duty-cycle representation in state space, driven by the nonadimensionalize approach to avoid unit inconsistencies in the model. We also consider a Direct Current (DC) analysis through the fractional Riemann–Liouville (R-L) approach. Moreover, the fractional order Buck-Boost converter model is implemented in the Matlab/Simulink setting, which is also powered by the Fractional-order Modeling and Control (FOMCON) toolbox. When modifying the fractional model order, we identify significant variations in the dynamic converter response from this simulated scenario. Finally, we detail how to achieve a fast dynamic response without oscillations and an adequate overshoot, appropriately varying the fractional-order coefficient. The numerical results have allowed us to determine that with the decrease of the fractional order, the model presents minor oscillations, obtaining an output voltage response six times faster with a significant overshoot reduction of 67%, on average.

Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyper-heuristics have on combinatorial optimization problems.

Hyper-heuristics have arisen as methods that increase the generality of existing solvers. They have proven helpful for dealing with complex problems, particularly those related to combinatorial optimization. Their recent growth in popularity has increased the daily amount of text in the related literature. This information is primarily unstructured, mainly text that traditional computer data systems cannot process. Traditional systematic literature review studies exhibit multiple limitations, including high time consumption, lack of replicability, and subjectivity of the results. For this reason, text mining has become essential for researchers in recent years. Therefore, efficient text mining techniques are needed to extract meaningful information, patterns, and relationships. This study adopts a literature review of 963 journal and conference papers on hyper-heuristic-related works. We first describe the essential text mining techniques, including text preprocessing, word clouds, clustering, and frequent association rule learning in hyper-heuristic publications. With that information, we implement visualization tools to understand the most frequent relations and topics in the hyper-heuristic domain. The main findings highlight the most dominant topics in the literature. We use text mining analysis to find widespread manifestations, representing the significance of the different areas of hyper-heuristics. Furthermore, we apply clustering to provide seven categories showing the associations between the topics related to hyper-heuristic literature. The vast amount of data available that we find opens up a new opportunity for researchers to analyze the status of hyper-heuristics and help create strategic plans regarding the scope of hyper-heuristics. Lastly, we remark that future work will address the limitations of collecting information from multiple data sources and analyze book chapters related to hyper-heuristics.

This article describes the SCSV_IPS software, developed for estimating the shear coefficient of spin viscosity. With that objective in mind, we built a mathematical model upon the Theory of Internal Angular Moment Diffusion. That coefficient, along with others, describes the movement of a ferrofluid confined to a cylindrical container under the effect of an external rotating magnetic field. As far as we know, there are no experimental measurement of such a coefficient. Therefore, to validate our software, we propose the use of synthetic measurements. We generate such type of data by adding white Gaussian noise signals to the numerical solution of the ferrohydrodynamic mathematical model. After that, we use it to solve a global optimization problem. We used different levels of noise as to increase the number of scenarios for our validation. We discuss some illustrative examples, which evidence the excellent performance of our software. Additionally, SCSV_IPS stands as a computational tool for validating experimental designs of future in situ measurements.

A better understanding of the behavior of asphaltenes is paramount for improving processes related to heavy crude oil, such as transport. Previous research has studied its aggregation [1], as well as its average chemical structure [2]. But, of course, this expands beyond the realm of oil, as other applications are affected by asphaltenes [3]. This work presents the collected data from electrodeposited asphaltenes. We used an H-type cell, with a capacity of 300 ml, and a bridge length of 30 cm. A constant voltage of 300 Vs (DC) was applied to steel electrodes of 3 cm × 9 cm. The generated electric field was of 1000 V/m, (E = Voltage (V)/distance (m)). The previously described assembly was modified, adding a dynamic magnetic field of 4 mT and 1% w/w of magnetic nanoparticles. Afterward, we analyzed deposits at the anode and cathode using the DSC-TGA Netzsch 449 F1 equipment. Through it, we gathered the calorimetric (DSC) and thermogravimetric (TGA) data. Moreover, the Proteus analysis software was used to generate DTGA data from the TGA values. The same procedure and analysis were repeated for asphaltenes of the same oil but obtained through precipitation with heptane. Our data may pave the road for research seeking to improve the extraction, transport and refining of heavy crudes. The reason: asphaltenes are responsible for the high viscosity of hydrocarbons. So, thermal processes are customary. Our thermogravimetric data may prove fruitful in the improvement and development of said processes.

There is a colourful palette of metaheuristics for solving continuous optimisation problems in the literature. Unfortunately, it is not easy to pick a suitable one for a specific practical scenario. Moreover, oftentimes the selected metaheuristic must be tuned until finding adequate parameter settings. Therefore, this work presents a framework based on a hyper-heuristic powered by Simulated Annealing for tailoring population-based metaheuristics. To do so, we recognise search operators from well-known techniques as building blocks for new ones. The presented framework comprises six main modules coded in Python, which can be used independently, and which help explore new metaheuristics.

En febrero de 2013 se realizó un estudio transversal para determinar la prevalencia de parásitos intestinales en habitantes de la comunidad rural “La Canoa” al sur del estado Anzoátegui. Se realizó un muestreo no probabilístico y voluntario. Las muestras fecales obtenidas por evacuación espontánea fueron analizadas mediante las técnicas de examen directo, Kato, sedimentación espontánea y coloración de Kinyoun. Para el análisis estadístico de los datos se usó la prueba Ji al cuadrado (c2) con un margen de seguridad de 95%, para demostrar la independencia entre las variables estudiadas. De los 158 habitantes evaluados, 95 resultaron parasitados para una prevalencia de 60,1%. Según la edad, todos los grupos fueron afectados sin diferencias estadísticamente significativas (χ2= 8,13 g.l.: 5 p>0,05). Ambos géneros resultaron parasitados por igual (p>0,05). Se observó 46,3% de poliparasitismo y 53,7% de monoparasitismo. Ocho especies de enteroparásitos fueron diagnosticados, siendo los protozoarios (98,9%) más prevalentes que los helmintos (1,1%). De los protozoarios, Blastocystis spp. fue el parásito más prevalente con 51,3%. Llamó la atención el hallazgo de un caso (0,6%) del complejo Entamoeba histolytica/E. dispar/ E. moshkovskii. Igualmente, cabe destacar el diagnóstico de dos casos (1,3%) de Cryptosporidium spp. Hymenolepis nana fue el único helminto encontrado (0,6%). Las asociaciones más comunes fueron Blastocystis spp.-Endolimax nana con 13 casos (29,5%) y Blastocystis spp.-Entamoeba coli con 12 casos (25%). En conclusión, se determinó una elevada prevalencia de parásitos intestinales (60,1%) en la comunidad rural “La Canoa” del estado Anzoátegui.

Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter. Current research suggests that, under certain circumstances, hyper-heuristics outperform single heuristics when evaluated in isolation. When hyper-heuristics are selected among existing algorithms, they map problem states into suitable solvers. Unfortunately, identifying the features that accurately describe the problem state—and thus allow for a proper mapping—requires plenty of domain-specific knowledge, which is not always available. This work proposes a simple yet effective hyper-heuristic model that does not rely on problem features to produce such a mapping. The model defines a fixed sequence of heuristics that improves the solving process of knapsack problems. This research comprises an analysis of feature-independent hyper-heuristic performance under different learning conditions and different problem sets.

Renewable energy sources are an environmentally attractive idea, but they require a proper control scheme to guarantee optimal operation. In this work, we tune different controllers for an Interleaved Boost Converter (IBC) powered by a photovoltaic array using three metaheuristics: Genetic Algorithm, Particle Swarm Optimization, and Gray Wolf Optimization. We also develop several controllers for a second simulated scenario where the IBC is plugged into an existing microgrid (MG) as this can provide relevant data for real-life applications. In both cases, we consider hybrid controllers based on a Linear Quadratic Regulator (LQR). However, we hybridize it with an Integral action (I-LQR) in the first scenario to compare our data against previously published controllers. In the second one, we add a Proportional-Integral technique (PI-LQR) as we do not have previous data to compare against to provide a more robust controller than I-LQR. To validate our approach, we run extensive simulations with each metaheuristic and compare the resulting data. We focus on two fronts: the performance of the controllers and the computing cost of the solvers when facing practical issues. Our results demonstrate that the approach proposed for tuning controllers is a feasible strategy. The controllers tuned with the metaheuristics outperformed previously proposed strategies, yielding solutions thrice faster with virtually no overshoot and a voltage ripple seven times smaller. Not only this, but our controllers could correct some issues liaised to the IBC when it is plugged into an MG. We are confident that these insights can help migrate this approach to a more diverse set of MGs with different renewable sources and escalate it to real-life experiments.

Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.

Many of the works conducted on algorithm selection strategies—methods that choose a suitable solving method for a particular problem—start from scratch since only a few investigations on reusable components of such methods are found in the literature. Additionally, researchers might unintentionally omit some implementation details when documenting the algorithm selection strategy. This makes it difficult for others to reproduce the behavior obtained by such an approach. To address these problems, we propose to rely on existing techniques from the Machine Learning realm to speed-up the generation of algorithm selection strategies while improving the modularity and reproducibility of the research. The proposed solution model is implemented on a domain-independent Machine Learning module that executes the core mechanism of the algorithm selection task. The algorithm selection strategies produced in this work are implemented and tested rapidly compared against the time it would take to build a similar approach from scratch. We produce four novel algorithm selectors based on Machine Learning for constraint satisfaction problems to verify our approach. Our data suggest that these algorithms outperform the best performing algorithm on a set of test instances. For example, the algorithm selectors Multiclass Neural Network (MNN) and Multiclass Logistic Regression (MLR), powered by a neural network and linear regression, respectively, reduced the search cost (in terms of consistency checks) of the best performing heuristic (KAPPA), on average, by 49% for the instances considered for this work.

Este trabajo considera la predicción en tiempo real de parámetros térmicos de una muestra sometida a radiación. Se estimaron la conductividad térmica y la capacidad calorífica. Se modeló el flujo volumétrico interno de calor uniforme y constante en el tiempo. Para resolver el problema inverso se usaron algoritmos de optimización global tales como el método de la espiral, el método de búsqueda en vórtice, el método de pesos ponderados, el método de enjambre de partículas unificado, el método optimización del campo electromagnético y el método de búsqueda armónica de ancho auto-regulado. Los resultados muestran que todos los algoritmos empleados estiman correctamente estos dos parámetros siempre y cuando, el SNR de las muestras (simuladas en este trabajo) esté por encima de 30 [dB]. Por ende, para propósitos prácticos, si se dispone de un buen diseño experimental y una buena instrumentación, se pueden estimar con alta precisión estos parámetros en tiempo real.

La presencia simultánea de varios parásitos puede tener importancia clínico-epidemiológica y también podría determinar la conducta terapéutica a seguir en la persona poliparasitada. Se realizó un estudio retrospectivo de tipo correlacional, partiendo de niños en edad escolares poliparasitados, se determinó el índice de afinidad de Fager para establecer las asociaciones entre los principales enteroparásitos identificados y determinar su importancia. Entre los años 2007 y 2016 fueron evaluadas 11 escuelas (10 en el municipio Heres y 1 en el municipio Caroní) y 1277 niños entre 5 y 15 años de edad. La distribución de los niños evaluados fue homogénea con relación al género ya que se estudiaron 48,0% (n=613) del género femenino y 52% (n=664) del masculino. De las 1277 muestras analizadas, 811 presentaron formas evolutivas de enteroparásitos, lo que indica un 63,7% de frecuencia. En orden los más comunes fueron los cromistas representado por Blastocystis spp. (49,3%), seguido de los protozoarios Endolimax nana (14,5%) y Entamoeba coli (11,7%), y en último lugar, los helmintos, donde el de mayor frecuencia fue Ascaris lumbricoides (5,5%). Los escolares resultaron poliparasitados en 43,2% de los casos, siendo el parásito más comúnmente asociado Blastocystis spp. con 90% de los casos, seguido de E. nana (43,1%) y E. coli (35,4%). Los índices de afinidad (IAB) y pruebas de “t” efectuadas, señalaron asociación entre Blastocystis spp. y los protozoarios G. intestinalis, E. nana y E. coli y entre los geohelmintos Trichuris trichiura y A. lumbricoides. En conclusión, las asociaciones que mostraron significancia estadística de acuerdo al índice de afinidad de Fager y al estudio estadístico fueron: entre el cromista Blastocystis spp. y los protozoarios G. intestinalis, E. nana y E. coli y entre los helmintos Ascaris lumbricoides-Trichuris trichiura.

Este trabajo considera la estimación de la generación interna volumétrica de calor y la capacidad calorífica de una muestra esférica sólida calentada por un campo electromagnético homogéneo variante en el tiempo. Para tal fin, la estrategia numérica soluciona el correspondiente problema inverso. Tres formas funcionales (lineal, senoidal y exponencial) para el campo electromagnético fueron considerados. Ruido blanco fue agregado al perfil de temperatura teórica (i.e. la solución del problema directo) para simular una situación más realística. La temperatura se pretendió que fuera leída por cuatro sensores. El problema inverso fue solucionado a través de tres diferentes enfoques: usando un optimizador tradicional, usando técnicas modernas y usando una mezcla de ambos. En el primer caso, usamos un algoritmo determinístico tradicional como lo es el de Levenberg-Marquardt (LM). En el segundo, consideramos tres metaheurísticos estocásticos: El Algoritmo de optimización de la espiral (SOA), la Búsqueda en vórtice (VS), y el método de atracción ponderada (WAM). Para el caso final, proponemos híbridos entre el LM y los algoritmos metahehurísticos. Los resultados muestran que LM converge a la solución esperada solo si las condiciones iniciales (IC) están dentro de un rango limitado. Por otra parte, los metaheurísticos convergen en un amplio rango de IC pero muestra baja precisión. Los enfoques híbridos convergen y mejoran la precisión obtenida con los metaheurísticos. La diferencia entre los valores esperados y obtenidos, así como, los errores RMS son reportados y comparados para los tres métodos.

This research article proposes the use of a novel variant of the HS algorithm (harmony search), i.e. SFHS (self-regulated fret width harmony search algorithm), for calculating and predicting fugacity and activity coefficients in binary mixtures. Parameter selection was carried out based on preliminary results with different standard test functions. Different previously reported systems were selected, at 25 ° C and 40 ° C, and at low and moderate pressure levels. Moreover, two solutes were selected: carbon dioxide and ethane. Different solvents, both polar and nonpolar, were selected with comparative purposes. Activity and fugacity coefficients were calculated using the Redlich-Kwong state equation and Lewis rule, along with the sfhs algorithm, assuming both solutes in vapor phase. Consistency of the activity coefficients was analyzed by the Redlich-Kister strategy. Results were very close to those found experimentally by other authors, and most of them did not differ in more than one percentage unit.

Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.

La pediculosis capitis es producida por Pediculus capitis, un insecto áptero que se ha especializado a vivir en el cuero cabelludo humano. Esta ectoparasitosis sigue siendo común en la actualidad especialmente en niños en edad escolar y con deficiencias sociosanitarias. El objetivo de este estudio de tipo transversal, realizado en junio de 2014, fue determinar la prevalencia de pediculosis capitis en habitantes de la comunidad indígena “Itopoicon”, estado Bolívar, Venezuela. Previa firma del consentimiento informado, a cada participante se le llenó una ficha clínico-epidemiológica y se le realizó un examen del pelo y cuero cabelludo.