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.

Using multi-variable grey model optimized by differential evolution algorithm to forecast plasma concentration of propofol / 药学学报

Due to the characteristics of propofol of high time-varying, and complex compartment model, the traditional method of nonlinear mixed effects modeling (NONMEM) has miscellaneous of variables and plenty of artificial factors in the estimation of propofol. This study was aimed to build a propofol prediction model based on the differential evolution (DE) algorithm and grey model. DE was used to optimize the pa-rameter of multi-variable grey model (MGM) and to build a model of prediction of the plasma concentration of propofol based on the grey model. It was compared with the results of NONMEM algorithm. In conclusion, the median performance error (MDPE) of DE-MGM was -4.6%, while the result of NONMEM is -12.13%. The median absolute performance error (MDAPE) of GA-BP neural network is 13.19%, while that of NONMEM is 23.12%. The experimental results suggest that the new method is suitable to determine the short half-life of anesthesia drug propofol with higher accuracy.

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.

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.