Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution.

Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK's layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation.

Automatic detection of semantic primitives using optimization based on genetic algorithm.

In this article, we propose a method for the automatic retrieval of a set of semantic primitive words from an explanatory dictionary and a novel evaluation procedure for the obtained set of primitives. The approach is based on the representation of the dictionary as a directed graph with a single-objective constrained optimization problem via a genetic algorithm with the PageRank scoring model. The problem is defined as a subset selection. The algorithm is fit to search for the sets of words that should fulfil several requirements: the cardinality of the set should not exceed empirically selected limits and the PageRank word importance score is minimized with cycle prevention thresholding. In the experiments, we used the WordNet dictionary for English. The proposed method is an improvement over the previous state-of-the-art solutions.

A 3D Approach Using a Control Algorithm to Minimize the Effects on the Healthy Tissue in the Hyperthermia for Cancer Treatment.

According to the World Health Organization, cancer is a worldwide health problem. Its high mortality rate motivates scientists to study new treatments. One of these new treatments is hyperthermia using magnetic nanoparticles. This treatment consists in submitting the target region with a low-frequency magnetic field to increase its temperature over 43 °C, as the threshold for tissue damage and leading the cells to necrosis. This paper uses an in silico three-dimensional Pennes' model described by a set of partial differential equations (PDEs) to estimate the percentage of tissue damage due to hyperthermia. Differential evolution, an optimization method, suggests the best locations to inject the nanoparticles to maximize tumor cell death and minimize damage to healthy tissue. Three different scenarios were performed to evaluate the suggestions obtained by the optimization method. The results indicate the positive impact of the proposed technique: a reduction in the percentage of healthy tissue damage and the complete damage of the tumors were observed. In the best scenario, the optimization method was responsible for decreasing the healthy tissue damage by 59% when the nanoparticles injection sites were located in the non-intuitive points indicated by the optimization method. The numerical solution of the PDEs is computationally expensive. This work also describes the implemented parallel strategy based on CUDA to reduce the computational costs involved in the PDEs resolution. Compared to the sequential version executed on the CPU, the proposed parallel implementation was able to speed the execution time up to 84.4 times.

Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning.

The present study investigates how to apply continuous tow shearing (CTS) in a manufacturable design parameterization to obtain reduced imperfection sensitivity in lightweight, cylindrical shell designs. The asymptotic nonlinear method developed by Koiter is applied to predict the post-buckled stiffness, whose index is constrained to be positive in the optimal design, together with a minimum design load. The performance of three machine learning methods, namely, Support Vector Machine, Kriging, and Random Forest, are compared as drivers to the optimization towards lightweight designs. The new methodology consists of contributions in the areas of problem modeling, the selection of machine learning strategies, and an optimization formulation that results in optimal designs around the compromise frontier between mass and stiffness. The proposed ML-based framework proved to be able to solve the inverse problem for which a target design load is given as input, returning as output lightweight designs with reduced imperfection sensitivity. The results obtained are compatible with the existing literature where hoop-oriented reinforcements were added to obtain reduced imperfection sensitivity in composite cylinders.

Optimal design of tuned mass dampers through differential evolution method for reducing the dynamic response of structures subjected to earthquake loads / Diseño óptimo de amortiguadores de masa sintonizada mediante evolución diferencial para reducir la respuesta dinámica de estructuras sujetas a cargas sísmicas / Projeto ótimo de amortecedores de massa usando evolução diferencial para reduzir a resposta dinâmica das estruturas sob cargas sísmicas

ABSTRACT This paper introduces a methodology for the optimal design of passive Tuned Mass Dampers (TMDs) to control the dynamic response of buildings subjected to earthquake loads. The selection process of the optimal design parameters is carried out through a metaheuristic approach based on differential evolution (DE) which is a fast, efficient, and precise technique that does not require high computational efforts. The algorithm is aimed to reduce the maximum horizontal peak displacement of the structure and the root mean square (RMS) response of displacements as well. Furthermore, four more objective functions derived from multiple weighted linear combinations of the two previously mentioned parameters are also studied to obtain the most efficient TMD design configuration. A parallel process based on an exhaustive search (ES) with precision to 2 decimal positions is used to validate the optimization methodology based on DE. The proposed methodology is then applied to a 32-story case-study derived from an actual building structure and subjected to different ground acceleration registers. The best dynamic performance of the building is observed when the greatest weight is given to the RMS response of displacement in the optimization process. Finally the numerical results reveal that the proposed methodology based on DE is effective in finding the optimal TMD design configuration by reducing the maximum floor displacement up to 4% and RMS values of displacement of up to 52% in the case-study building.

RESUMEN Este artículo presenta una metodología para el diseño óptimo de Amortiguadores de Masa Sintonizada (AMS) para el control de la respuesta dinámica de edificios sometidos a cargas sísmicas. El proceso de selección de los parámetros óptimos de diseño se realiza mediante un enfoque metaheurístico basado en Evolución Diferencial (ED) la cual es una técnica rápida, eficiente y precisa que no requiere grandes esfuerzos computacionales. El algoritmo tiene como objetivo reducir el desplazamiento de pico horizontal máximo de la estructura y también la media cuadrática (Valor eficaz) de desplazamientos. Adicionalmente, se estudian otras cuatro funciones objetivo derivadas de múltiples combinaciones lineales ponderadas de los dos parámetros mencionados anteriormente para obtener la configuración de diseño del AMS más eficiente. De forma paralela, se utiliza un proceso basado en una búsqueda exhaustiva (ES) con precisión a 2 posiciones decimales para validar la metodología de optimización basada en DE. Posteriormente, la metodología propuesta se aplica a un caso de estudio derivado de un edificio real de 32 pisos sometido a diferentes registros sísmicos de aceleración del suelo. Se observa un mejor comportamiento dinámico del edificio cuando se le da el mayor peso a la respuesta RMS de desplazamiento en el proceso de optimización. Finalmente, los resultados numéricos revelan que la metodología propuesta basada en DE es efectiva para encontrar la configuración óptima de diseño de TMD al reducir el desplazamiento máximo del piso hasta en un 43% y los valores RMS de desplazamiento de hasta el 52% en el caso de estudio.

RESUMO Este artigo apresenta uma metodologia para a otimização de amortecedores de massa sintonizados (TMD) para o controle da resposta dinâmica de edifícios sujeitos a cargas sísmicas. O processo de seleção dos parâmetros ótimos é realizado mediante uma abordagem metaheunstica baseada na Evolução Diferencial (DE) que é uma técnica rápida, eficiente e precisa que não requer de grandes esforços computacionais. O algoritmo visa reduzir o deslocamento máximo do pico horizontal da estrutura e também os deslocamentos da raiz quadrada média (RMS). Além disso, quatro outras funções objetivo derivadas de distintas combinações lineares ponderadas dos dois parâmetros de resposta já mencionados, são estudadas para obter a configuração de TMD mais eficiente. Em paralelo, um processo de busca exaustiva (ES) com precisão de 2 casas decimais é usado para validar a metodologia de otimização baseada na DE. Posteriormente, a metodologia proposta é aplicada a um caso de estudo derivado de um edifício real de 32 andares sujeito a diferentes registros de aceleração sísmica do solo. É observado um melhor comportamento dinâmico do edifício quando é dada uma maior ponderação no processo de otimização à resposta de deslocamento RMS. Finalmente, os resultados numéricos revelam que a metodologia proposta fundamentada na DE é eficaz para encontrar os parâmetros ótimos do TMD, reduzindo o pico de deslocamento máximo em até 43% e os valores de deslocamento RMS em até 52% no caso estudado.

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution.

This article presents an approach to solve the inverse kinematics of cooperative mobile manipulators for coordinate manipulation tasks. A self-adaptive differential evolution algorithm is used to solve the inverse kinematics as a global constrained optimization problem. A kinematics model of the cooperative mobile manipulators system is proposed, considering a system with two omnidirectional platform manipulators with n DOF. An objective function is formulated based on the forward kinematics equations. Consequently, the proposed approach does not suffer from singularities because it does not require the inversion of any Jacobian matrix. The design of the objective function also contains penalty functions to handle the joint limits constraints. Simulation experiments are performed to test the proposed approach for solving coordinate path tracking tasks. The solutions of the inverse kinematics show precise and accurate results. The experimental setup considers two mobile manipulators based on the KUKA Youbot system to demonstrate the applicability of the proposed approach.

Reconstruction of a gene regulatory network of the induced systemic resistance defense response in Arabidopsis using boolean networks.

BACKGROUND: An important process for plant survival is the immune system. The induced systemic resistance (ISR) triggered by beneficial microbes is an important cost-effective defense mechanism by which plants are primed to an eventual pathogen attack. Defense mechanisms such as ISR depend on an accurate and context-specific regulation of gene expression. Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRNs). Here, we explore the regulatory mechanism of the ISR defense response triggered by the beneficial bacterium Paraburkholderia phytofirmans PsJN in Arabidopsis thaliana plants infected with Pseudomonas syringae DC3000. To achieve this, a GRN underlying the ISR response was inferred using gene expression time-series data of certain defense-related genes, differential evolution, and threshold Boolean networks. RESULTS: One thousand threshold Boolean networks were inferred that met the restriction of the desired dynamics. From these networks, a consensus network was obtained that helped to find plausible interactions between the genes. A representative network was selected from the consensus network and biological restrictions were applied to it. The dynamics of the selected network showed that the largest attractor, a limit cycle of length 3, represents the final stage of the defense response (12, 18, and 24 h). Also, the structural robustness of the GRN was studied through the networks' attractors. CONCLUSIONS: A computational intelligence approach was designed to reconstruct a GRN underlying the ISR defense response in plants using gene expression time-series data of A. thaliana colonized by P. phytofirmans PsJN and subsequently infected with P. syringae DC3000. Using differential evolution, 1000 GRNs from time-series data were successfully inferred. Through the study of the network dynamics of the selected GRN, it can be concluded that it is structurally robust since three mutations were necessary to completely disarm the Boolean trajectory that represents the biological data. The proposed method to reconstruct GRNs is general and can be used to infer other biologically relevant networks to formulate new biological hypotheses.

Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms.

In supply chain management, fast and accurate decisions in supplier selection and order quantity allocation have a strong influence on the company's profitability and the total cost of finished products. In this paper, a novel and non-linear model is proposed for solving the supplier selection and order quantity allocation problem. The model is introduced for minimizing the total cost per time unit, considering ordering, purchasing, inventory, and transportation cost with freight rate discounts. Perfect rate and capacity constraints are also considered in the model. Since metaheuristic algorithms have been successfully applied in supplier selection, and due to the non-linearity of the proposed model, particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), are implemented as optimizing solvers instead of analytical methods. The model is tested by solving a reference model using PSO, GA, and DE. The performance is evaluated by comparing the solution to the problem against other solutions reported in the literature. Experimental results prove the effectiveness of the proposed model, and demonstrate that metaheuristic algorithms can find lower-cost solutions in less time than analytical methods.

Design of a robotic device actuated by cables for human lower limb rehabilitation using self-adaptive differential evolution and robust optimization / Dispositivo robótico atuado por cabos para reabilitação do membro inferior humano utilizando evolução diferencial auto-adaptável e otimização robusta

In engineering designed systems it is commonly considered that mathematical models, variables, and parameters are sufficiently reliable, i.e., there are no errors in modeling and estimation. However, the systems to be optimized can be sensitive to small changes in the designed variables causing significant changes in the objective function. Robust optimization is an approach for modeling optimization problems under uncertainty in which the modeler aims to find decisions that are optimal for the worst-case realization of the uncertainties within a given set of values. In this contribution, a self-adaptive heuristic optimization method, namely the Self-Adaptive Differential Evolution (SADE), is evaluated. Differently from the canonical Differential Evolution algorithm (DE), the SADE strategy is able to update the required parameters such as population size, crossover parameter, and perturbation rate, dynamically. This is done by considering a defined convergence rate on the evolution process of the algorithm in order to reduce the number of evaluations of the objective function. For illustration purposes, the SADE strategy is associated with the Mean Effective Concept (MEC) for insertion robustness, is applied to minimize forces applied in cables used for the rehabilitation of the human lower limbs by determining the positioning of motors. The results show that the methodology that was proposed (SADE+MEC) appears as an interesting strategy for the treatment of robust optimization problems.

No projeto de sistemas de engenharia é comum considerar que os modelos, as variáveis e os parâmetros são confiáveis, isto é, não apresentam erros de modelagem e de estimação. Entretanto, os sistemas a serem otimizados podem ser sensíveis a pequenas alterações nas variáveis de projeto causando significativas modificações no vetor de objetivos. Otimização robusta é uma abordagem para modelagem de problemas de otimização sob incerteza em que o modelador tem como objetivo encontrar decisões que são ideais para o pior caso de realização das incertezas dentro de um determinado conjunto de valores. Neste trabalho, um método de otimização heurística auto-adaptável, nomeada Self-Adaptive Differential Evolution (SADE), é avaliada. Diferentemente do algoritmo de Evolução Diferencial, a estratégia SADE é capaz de atualizar os parâmetros necessários, tais como o tamanho da população, o parâmetro de passagem e taxa de perturbação, de forma dinâmica. Isto é feito considerando uma taxa de convergência definido no processo de evolução do algoritmo, a fim de reduzir o número de avaliações da função objetivo. Para fins de ilustração, a estratégia SADE associado ao conceito de média efetiva, para inserção da robustez, é aplicada para minimizar as forças aplicadas nos cabos da estrutura robótica utilizada para a reabilitação dos membros inferiores humanos, determinando o posicionamento dos atuadores. Os resultados mostram que o método proposto neste trabalho configura-se como uma estratégia interessante para o tratamento de problemas de otimização robustos.

Energy Efficient Clustering in Multi-hop Wireless Sensor Networks Using Differential Evolutionary MOPSO

ABSTRACT The primary challenge in organizing sensor networks is energy efficacy. This requisite for energy efficacy is because sensor nodes capacities are limited and replacing them is not viable. This restriction further decreases network lifetime. Node lifetime varies depending on the requisites expected of its battery. Hence, primary element in constructing sensor networks is resilience to deal with decreasing lifetime of all sensor nodes. Various network infrastructures as well as their routing protocols for reduction of power utilization as well as to prolong network lifetime are studied. After analysis, it is observed that network constructions that depend on clustering are the most effective methods in terms of power utilization. Clustering divides networks into inter-related clusters such that every cluster has several sensor nodes with a Cluster Head (CH) at its head. Sensor gathered information is transmitted to data processing centers through CH hierarchy in clustered environments. The current study utilizes Multi-Objective Particle Swarm Optimization (MOPSO)-Differential Evolution (DE) (MOPSO-DE) technique for optimizing clustering.

Application of Anaerobic Digestion Model No. 1 to describe the syntrophic acetate oxidation of poultry litter in thermophilic anaerobic digestion.

A molecular analysis found that poultry litter anaerobic digestion was dominated by hydrogenotrophic methanogens which suggests that bacterial acetate oxidation is the primary pathway in the thermophilic digestion of poultry litter. IWA Anaerobic Digestion Model No. 1 (ADM1) was modified to include the bacterial acetate oxidation process in the thermophilic anaerobic digestion (TAD). Two methods for ADM1 parameter estimation were applied: manual calibration with non-linear least squares (MC-NLLS) and an automatic calibration using differential evolution algorithms (DEA). In terms of kinetic parameters for acetate oxidizing bacteria, estimation by MC-NLLS and DEA were, respectively, km 1.12 and 3.25 ± 0.56 kg COD kg COD(-1)d(-1), KS 0.20 and 0.29 ± 0.018 kg COD m(-3) and Yac-st 0.14 and 0.10 ± 0.016 kg COD kg COD(-1). Experimental and predicted volatile fatty acids and biogas composition were in good agreement. Values of BIAS, MSE or INDEX demonstrate that both methods (MC-NLLS and DEA) increased ADM1 accuracy.

Enhanced lipase recovery through RSM integrated differential evolutionary approach from the fermented biomass

The aim of this work was to apply a modeling integrated optimisation approach for a complex, highly nonlinear system for an extracellular lipase extraction process. The model was developed using mutation, crossover and selection variables of Differential Evolution (DE) based on central composite design of Response Surface Methodology. The experimentally validated model was optimized by DE, a robust evolutionary optimization tool. A maximum lipase activity of 134.13 U/gds (more than 36.28 U/gds compared to one variable at a time approach) was observed with the DE-stated optimum values of 25.01% dimethyl sulfoxide concentration, 40 mM buffer, 128.52 min soaking time and 35ºC with the DE control parameters, namely number of population, generations, crossover operator and scaling factor as 20, 50, 0.5 and 0.25, respectively. The use of DE approach improved the optimization capability and decision speed, resulting in an improved yield of 36.28 U/gds compared to the one variable at a time approach for the extracellular lipase activity under the non-optimized conditions. The developed mathematical model and optimization were generic in nature, which seemed to be useful for the scale-up studies of maximum recovery of lipase from the fermented biomass.