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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.

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.

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.

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.

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.

This research article, supported by Universidad Industrial de Santander (Vicerrectoría de Investigación y Extensión), shows the results achieved after using the electromagnetic cavity of a microwave oven and a tem­perature measurement system, to heat a mineral sample from the mining district of Vetas, in order to see if it was possible to induce a cheaper processing. A factorial design of experiment (DOE) was used to observe the effect of the microwave power, the exposure time and the sample’s mass. It was found that an energy consumption reduction of 81.7%, and a time reduction of 75%, were possible to achieve while maintaining the recovery margin at 90% of the achieved with a traditional roasting, at lab level. Thus, it can be concluded that, at least for this ore, microwave roasting of the sample is viable at this scale. Moreover, if a better appli­cator were to be used, the benefits could be enhanced even more, so it is encouraged as a next step.

En este artículo se presenta una estrategia novedosa para la búsqueda de múltiples soluciones en sistemas de ecuaciones no lineales, mediante la metaheurística de optimización por enjambre de partículas, y su versión unificada. Inicialmente, se expone una sección de fundamentos, donde se muestran las bases de las técnicas numéricas utilizadas y también la transformación del problema de solución en uno de optimización. Se comprueba esta nueva estrategia con diferentes tipos de sistemas, variando sistemáticamente el tamaño de los mismos para observar el comportamiento de las metaheurísticas usadas. Luego de analizar los datos obtenidos, se encuentra que la estrategia implementada tiene un gran potencial de aplicación en problemas de ingeniería. Para los sistemas analizados en este escrito, se recomienda utilizar la versión unificada o la original, dependiendo de si el sistema es pequeño o grande (menor o mayor a cinco ecuaciones e incógnitas), respectivamente.

This research article describes experiments using several Asymmetrical Capacitors prototypes (AsC) with the objective to reproduce this unusual physical phenomenon, as well as to show its exceptional characteristics. Some results are published for the first time. The operating voltage was increased up to 31kV using a DC generator. The AsC levitated in every experiment, simultaneously generating a gas current from the lowest superficial area electrode. Although this phenomenon has been known for over one century, its scientific basis remains unknown and there isn't a model that properly explains its behavior under different conditions. It was proved that it works as a conventional electrostatic separator because it eliminated solid particles from a gas sample either confined in a container or flowing, with the advantage of low energy requirements. In the same way, at operating conditions, it produces ozone in a controlled way, as in the corona effect.El presente artículo de investigación describe experimentos realizados utilizando prototipos de condensadores asimétricos (CAs), con el fin de reproducir este inusual fenómeno físico y mostrar algunas de sus excepcionales características. Algunos resultados aparecen por primera vez en la literatura. Se utilizó un generador de 31kV CC y se observó que en todos los casos el CAs levitó, generándose simultáneamente una corriente de gases en el electrodo de menor área superficial. Aunque este fenómeno se conoce hace más de un siglo, su fundamento científico es aún desconocido y no hay un modelo que explique satisfactoriamente su comportamiento a diferentes condiciones experimentales. Se comprobó que opera en forma equivalente a un separador electrostático convencional eliminando partículas sólidas de un gas encerrado en un recipiente o en flujo, con la ventaja de un bajo consumo de energía eléctrica. Igualmente, a las condiciones de operación, produce ozono de forma controlada, similar al efecto corona.

In this review article, an electromagnetic heating time evolution is presented, stating a brief description of the main applications in some of the top fields regarding this technology and striving to indicate fields for further research in an area that becomes more important with each passing day. It was found that the food industry and that plasma related products are the most important branches of this technology, where a vast majority has focused on microwaves instead of using radiofrequency. Some other areas are also discussed and at the end possible future researches are outlined, such as a mixed heating dryer and a sulfur lamp.Este artículo de revisión presenta la forma como ha evolucionado la utilización de energía electromagnética para generar calentamiento, mencionando algunos campos clave de esta tecnología en aras de indicar nichos y posibles campos de investigación de un campo que cada vez toma más fuerza. Se encontró que las aplicaciones principales se centran alrededor del procesamiento de comida y de aplicaciones de plasma, utilizando en su mayoría microondas en vez de radiofrecuencia. Al final se enumeran algunas áreas que, desde el punto de vista de los autores, merecen mayor nivel de investigación, como son secadores de calentamiento mixto y la lámpara de azufre.