Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

A universal segment approach for the prediction of the activity coefficient.

Doctor of Philosophy in Chemical Engineering. University of KwaZulu-Natal, Durban 2016.This study comprised an investigation into solid-liquid equilibrium prediction, measurement and modelling for active pharmaceutical ingredients, and solvents, employed in the pharmaceutical industry. Available experimental data, new experimental data, and novel measuring techniques, as well as existing predictive thermodynamic activity coefficient model revisions, were investigated. Thereafter, and more centrally, a novel model for the prediction of activity coefficients, at solid-liquid equilibrium, which incorporates global optimization strategies in its training, is presented. The model draws from the segment interaction (via segment surface area), approach in solidliquid equilibrium modelling for molecules, and extends this concept to interactions between functional groups. Ultimately, a group-interaction predictive method is proposed that is based on the popular UNIFAC-type method (Fredenslund et al. 1975). The model is termed the Universal Segment Activity Coefficient (UNISAC) model. A detailed literature review was conducted, with respect to the application of the popular predictive models to solid-liquid phase equilibrium (SLE) problems, involving structurally complex solutes, using experimental data available in the literature (Moodley et al., 2016 (a)). This was undertaken to identify any practical and theoretical limitations in the available models. Activity coefficient predictions by the NRTL-SAC ((Chen and Song 2004), Chen and Crafts, 2006), UNIFAC (Fredenslund et al., 1975), modified UNIFAC (Dortmund) (Weidlich and Gmehling, 1987), COSMO-RS (OL) (Grensemann and Gmehling, 2005), and COSMOSAC (Lin and Sandler, 2002), were carried out, based on available group constants and sigma profiles, in order to evaluate the predictive capabilities of these models. The quality of the models is assessed, based on the percentage deviation between experimental data and model predictions. The NRTL-SAC model is found to provide the best replication of solubility rank, for the cases tested. It, however, was not as widely applicable as the majority of the other models tested, due to the lack of available model parameters in the literature. These results correspond to a comprehensive comparison conducted by Diedrichs and Gmehling (2011). After identifying the limitations of the existing predictive methods, the UNISAC model is proposed (Moodley et al, 2015 (b)). The predictive model was initially applied to solid-liquid systems containing a set of 18 structurally diverse, complex pharmaceuticals, in a variety of solvents, and compared to popular qualitative solubility prediction methods, such as NRTLSAC and the UNIFAC based methods. Furthermore, the Akaike Information Criterion (AIC) (Akaike, 1974) and Focused Information Criterion (FIC) (Claeskens and Hjort, 2003) were used to establish the relative quality of the solubility predictions. The AIC scores recommend the UNISAC model for over 90% of the test cases, while the FIC scores recommend UNISAC in over 75% of the test cases. The sensitivity of the UNISAC model parameters was highlighted during the initial testing phase, which indicated the need to employ a more rigorous method of determining parameters of the model, by optimization to the global minimum. It was decided that the Krill Herd algorithm optimization technique (Gandomi and Alavi, 2012), be employed to accomplish this. To verify the suitability of this decision, the algorithm was applied to phase stability (PS) and phase equilibrium calculations in non-reactive (PE) and reactive (rPE) systems, where global minimization of the total Gibbs energy is necessary. The results were compared to other methods from the literature (Moodley et al., 2015 (c)). The Krill Herd algorithm was found to reliably determine the desired global optima in PS, PE and rPE problems. The algorithm outperformed or matched all other methods considered for comparison, including swarm intelligence and genetic algorithms, with an average success rate of 89.5 %, and with an average number of function evaluations of 1406. The UNISAC model was then reviewed, and extended, to incorporate the significantly more detailed group fragmentation scheme of Moller et al. (2008), to improve the range of application of the model. New UNISAC segment group area parameters that were obtained by data fitting, using the Krill Herd Algorithm as an optimization tool, were calculated. This Extended UNISAC model was then used to predict SLE compositions, or temperatures, of a large volume of experimental binary and ternary system data, available in the literature, (over 4000 data points), and was compared to predictions by the UNIFAC-based and COSMO-based models (Moodley et al., 2016 (d)). The AIC scores suggest that the Extended UNISAC model is superior to the original UNIFAC, modified UNIFAC (Dortmund) (2013), COSMO-RS(OL), and COSMO-SAC models, with relative AIC scores of 1.95, 4.17, 2.17 and 2.09. In terms of percentage deviations alone between experimental and predicted values, the modified UNIFAC (Dortmund) model, and original UNIFAC models, proved superior at 21.03% and 29.03% respectively; however, the Extended UNISAC model was a close third at 32.99%. As a conservative measure to ensure that inter-correlation of the training set did not occur, previously unmeasured data was desired as a test set, to verify the ability of the Extended UNISAC model to estimate data outside of the training set. To accomplish this, SLE measurements were conducted for the systems diosgenin/ estriol/ prednisolone/ hydrocortisone/ betulin and estrone. These measurements were undertaken in over 10 diverse organic solvents, and water, at atmospheric pressure, within the temperature range 293.2-328.2 K, by employing combined digital thermal analysis and thermal gravimetric analysis, to determine compositions at saturation (Moodley et al., 2016 (e), Moodley et al., 2016 (f), Moodley et al., 2016 (g)). This previously unmeasured test set data was compared to predictions by the Extended UNISAC, UNIFAC-based and COSMO-based methods. It was found that the Extended UNISAC model can qualitatively predict the solubility in the systems measured (where applicable), comparably to the other popular methods tested. The desirable advantage is that the number of model parameters required to describe mixture activities is far lower than for the group contribution and COSMO-based methods. Future developments of the Extended UNISAC model were then considered, which included the preliminary testing of alternate combinatorial expressions, to better account for size-shape effects on the activity coefficient. The limitations of the Extended UNISAC model are also discussed

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Application of Metaheuristics in Signal Optimisation of Transportation Networks: A Comprehensive Survey

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.With rapid population growth, there is an urgent need for intelligent traffic control techniques in urban transportation networks to improve the network performance. In an urban transportation network, traffic signals have a significant effect on reducing congestion, improving safety, and improving environmental pollution. In recent years, researchers have been applied metaheuristic techniques for signal timing optimisation as one of the practical solution to enhance the performance of the transportation networks. Current study presents a comprehensive survey of such techniques and tools used in signal optimisation of transportation networks, providing a categorisation of approaches, discussion, and suggestions for future research

Sustainable Structural Design for High-Performance Buildings and Infrastructures

Exceptional design loads on buildings and structures may have different causes, including high-strain natural hazards, man-made attacks and accidents, and extreme operational conditions. All of these aspects can be critical for specific structural typologies and/or materials that are particularly sensitive. Dedicated and refined methods are thus required for design, analysis, and maintenance under structures’ expected lifetimes. Major challenges are related to the structural typology and material properties. Further issues are related to the need for the mitigation or retrofitting of existing structures, or from the optimal and safe design of innovative materials/systems. Finally, in some cases, no design recommendations are available, and thus experimental investigations can have a key role in the overall process. For this SI, we have invited scientists to focus on the recent advancements and trends in the sustainable design of high-performance buildings and structures. Special attention has been given to materials and systems, but also to buildings and infrastructures that can be subjected to extreme design loads. This can be the case of exceptional natural events or unfavorable ambient conditions. The assessment of hazard and risk associated with structures and civil infrastructure systems is important for the preservation and protection of built environments. New procedures, methods, and more precise rules for safety design and the protection of sustainable structures are, however, needed

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Bio-inspired Computing and Smart Mobility

Por último, se aborda la predicción de plazas libres de aparcamiento utilizando técnicas de aprendizaje automático, tales como series temporales, agrupamiento, etc., incluyendo un prototipo de aplicación web. La tercera parte de esta tesis doctoral se enfoca en el diseño y evaluación de un nuevo algoritmo inspirado en la epigénesis, el Algoritmo Epigenético. Luego de la descripción del modelo en el que se basa y de sus partes, se utiliza este nuevo algoritmo para la resolución del problema de la mochila multidimensional y se comparan sus resultados con los de otros algoritmos del estado de arte. Por último se emplea también el Algoritmo Epigenético para la optimización de la arquitectura Yellow Swarm, un problema de movilidad inteligente resuelto por un nuevo algoritmo bioinspirado. A lo largo de esta tesis doctoral se han descrito los problemas de movilidad inteligente y propuesto nuevas herramientas para su optimización. A partir de los experimentos realizados se concluye que estas herramientas, basadas en algoritmos bioinspirados, son eficientes para abordar estos problemas, obteniendo resultados competitivos comparados con los del estado del arte, los cuales han sido validados estadísticamente. Esto representa un aporte científico pero también una serie de mejoras para la sociedad toda, tanto en su salud como en el aprovechamiento de su tiempo libre. Fecha de lectura de Tesis: 01 octubre 2018.Esta tesis doctoral propone soluciones a problemas de movilidad inteligente, concretamente la reducción de los tiempos de viajes en las vías urbanas, las emisiones de gases de efecto invernadero y el consumo de combustible, mediante el diseño y uso de nuevos algoritmos bioinspirados. Estos algoritmos se utilizan para la optimización de escenarios realistas, cuyo trazado urbano se obtiene desde OpenStreetMap, y que son luego evaluados en el microsimulador SUMO. Primero se describen las bases científicas y tecnológicas, incluyendo la definición y estado del arte de los problemas a abordar, las metaheurísticas que se utilizarán durante el desarrollo de los experimentos, así como las correspondientes validaciones estadísticas. A continuación se describen los simuladores de movilidad como principal herramienta para construir y evaluar los escenarios urbanos. Por último se presenta una propuesta para generar tráfico vehicular realista a partir de datos de sensores que cuentan el número de vehículos en la ciudad, utilizando herramientas incluidas en SUMO combinadas con algoritmos evolutivos. En la segunda parte se modelan y resuelven problemas de movilidad inteligente utilizando las nuevas arquitecturas Red Swarm y Green Swarm para sugerir nuevas rutas a los vehículos utilizando nodos con conectividad Wi-Fi. Red Swarm se centra en la reducción de tiempos de viajes evitando la congestión de las calles, mientras que Green Swarm está enfocado en la reducción de emisiones y consumo de combustible. Luego se propone la arquitectura Yellow Swarm que utiliza una serie de paneles LED para indicar desvíos que los vehículos pueden seguir en lugar de nodos Wi-Fi haciendo esta propuesta más accesible. Además se propone un método para genera rutas alternativas para los navegadores GPS de modo que se aprovechen mejor las calles secundarias de las ciudades, reduciendo los atascos

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management