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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.

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.

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.

El presente artículo propone utilizar una versión discreta del bien conocido algoritmo metaheurístico de optimización por enjambre de partículas, DPSO, para solucionar numéricamente un sistema de ecuaciones Diofánticas lineales. Así mismo, se muestra la transformación de este tipo de problema (es decir, la solución de un sistema de ecuaciones), en uno de optimización. El presente algoritmo es capaz de encontrar todas las raíces enteras en un dominio de búsqueda dado, al menos para los ejemplos mostrados. Se utilizan algunos problemas sencillos para verificar su eficacia. Además, se muestran algunos aspectos relacionados con el tiempo de procesamiento, así como con el efecto de incrementar la población y el dominio de búsqueda. Se encontró que la estrategia mostrada aquí representa una propuesta adecuada para trabajar con sistemas que tienen más incógnitas que ecuaciones, o cuando se tiene un tamaño considerable, debido a que se requiere un gran dominio de búsqueda.

Este artículo compara el algoritmo original ba sado en luciérnagas contra dos versiones su geridas por los autores. Se encontró q ue al utilizar algunas modificaciones propuestas en el documento, el tiem po de convergencia del algoritmo se reduce, mientras aumenta su precisión (es decir, converge con menos error). Por tanto, se recomienda fuertemente qu e estas variantes sean analizadas con má s detalle, especialmente para la solución de sistemas de ecuaciones no-lineales, dado que la prue ba exploratoria consignada aquí entregó b uenos resultados (es decir, las raíces encontradas por el algoritmo se encuentran muy cercanas a los valores teóricos). Además, se en contró que el algoritmo es dependi ente del tamaño del enjambre, en especial cuando se tr abaja con regiones planas.

El presente artículo muestra los principales resultados obtenidos al realizar la tostación con ondas electromagnéticas a un mineral de oro. Se escogió una mena del sur de Bolívar (Colombia), debido entre otras razones, a que los mineros de dicha zona suelen tener problemas para recuperar el mineral precioso por medios tradicionales. Se encontró que al aplicar ondas electromagnéticas en el rango de las microondas, el tiempo de tostación se redujo entre el 77 y el 83%, una disminución promedio cercana al 88% del consumo de energía eléctrica y se logró una recuperación de aproximadamente el 90% (peso/peso) de la obtenida con una tostación tradicional a nivel de laboratorio. Esto indica que es una alternativa que justifica estudios más profundos a escala de planta piloto.

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.

El presente artículo describe una estrategia numérica orientada hacia la solución de sistemas de ecuaciones no lineales que con frecuencia aparecen en el análisis de circuitos electrónicos. Esta estrategia se fundamenta en el uso del algoritmo de optimización de enjambre de partículas (PSO), como alternativa al tradicionalNewton-Raphson. Se tomó inicialmente, a título de ejemplo demostrativo, un circuito compuesto de dos resistencias lineales y un diodo. Seguidamente se utiliza otro ligeramente más complejo, constituido por una fuente de corriente, cuatro resistencias y dos diodos. Se encontró que el PSO posee mayor robustez frente al método tradicional. Fundamentado en estos resultados, se observó que esta alternativa de solución tiene características muy atractivas cuando se requiere solucionar este tipo de circuitos independientemente de su tamaño y complejidad.

El presente artículo propone utilizar una estrategia numérica basada en un algoritmo de optimización metaheurístico de enjambre de partículas discreto, para resolver un problema de diseño de compensadores en sistemas retroalimentados variantes en el tiempo. Se demuestra inicialmente como se puede convertir el problema de solución del sistema de ecuaciones Diofánticas lineales resultante en la solución de un problema de optimización. Se desarrollan ejemplos demostrativos que ilustran la idea principal. Se lograron soluciones de excelente calidad en cuanto a precisión y exactitud en tiempos de computación relativamente breves.

Este artículo describe una estrategia numérica de optimización metaheurística, utilizada para determinar las soluciones en el conjunto de los números reales de un sistema de ecuaciones no lineales. Se utiliza un teorema que demuestra la validez de conversión de un problema de solución de ecuaciones no lineales en otro de optimización. A título de demostración, se presentan algunos resultados para sistemas de dos, tres y cinco ecuaciones.