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In this study, the thermophysical properties of thermal conductivity and viscosity of a motor oil nanofluid were investigated using experimental data and artificial neural network. NSGA II optimization algorithm was used to maximize thermal conductivity and minimum viscosity with changes in temperature and volume fraction of nanofluids. Also, to obtain the viscosity and thermal conductivity values in terms of nanofluid temperature and volume fraction with 174 experimental data, neural network modeling was performed. Input data include temperature and volume fraction, and output is viscosity and thermal conductivity. Various indices such as R squared and Mean Square Error (MSE) have been used to evaluate the accuracy of modeling in the prediction of viscosity and thermal conductivity of nanofluids. The coefficient of determination R squared is 0.9989 indicating acceptable agreement with the experimental data. In order to optimize and finally results as an objective function, the optimization algorithm is presented and the Parto front and its corresponding optimum points are presented where the maximum optimization results of thermal conductivity and viscosity occur at 1% volume fraction.

In order to reach a sustainable planet, there is a permanent need by the consumer (decision- agent), to achieve sustainable solutions, with its decisions. Given the importance of the buildings, as a sector to achieve such solutions, as well as the diversity of household appliances existent on the market, with all its different issues, there are several tradeoffs to consider (e.g. energy and water consumption vs initial investment), which difficult the consumer’s choices from the market. The problem increases, since nowadays, the consumer tries to get a solution from the market, with a good compromise between the Economic, Social and Environmental dimensions, and according to its specific needs, which can be different from other consumers. By considering a multicriteria approach, combined with an optimization technique, based on Evolutionary Algorithms (EA), it’s provided a set of sustainable solutions from the market to the consumer that respects the compromise referred before. In this work, it is presented an approach to support a decision-agent (DA) (consumer), by performing a set of sustainable choices based on household appliances from the market and suitable to its needs. Based on the obtained solutions, several savings are achieved (electrical and water consumption, CO2 emissions), by considering the consumer’s relative importance, regarding its Economics, Environmental and Social concerns.

This study presents the optimization of a multilayer wire-on-tube condenser exposed to forced convection, using the Optimized Multi-objective Particle Swarm Optimization (OMOPSO) algorithm. The maximization of the heat transfer and the minimization of the heat exchange area were defined as objective functions. In the optimization process, the variations of eight geometric parameters of the condenser were analyzed, and the Multi-objective Evolutionary Algorithm based on Decomposition (MOEAD), Non-dominated Sorting Genetic Algorithm-II (NSGAII), and OMOPSO algorithms were statistically explored. Furthermore, the condenser optimization analysis was extended to the use of alternative refrigerants to R134a such as R600a and R513A. Among the relevant results, it can be commented that the OMOPSO algorithm presented the best option from the statistical point of view compared to the other two algorithms. Thus, optimal designs for the wire-on-tube condenser were defined for three proposed study cases and for each refrigerant, providing an overview of compact designs. Likewise, the reduction of the condenser area was analyzed in more detail, presenting a maximum reduction of 15% for the use of R134a compared to for the current design. Finally, the crossflow condition was studied with respect to the current one, concluding in a greater heat transfer and a smaller heat exchange surface.

Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.

This paper compares the three state-of-the-art algorithms when applied to a real-world case of the wind energy sector. Optimum locations are suggested for a wind farm by considering only Round 3 zones around the UK. The problem comprises of some of the most important techno-economic life cycle cost-related factors, which are modelled using the physical aspects of each wind farm location (i.e., the wind speed, distance from the ports, and water depth), the wind turbine size, and the number of turbines. The model is linked to NSGA II, NSGA III, and SPEA 2 algorithms, to conduct an optimisation search. The performance of these three algorithms is demonstrated and analysed, so as to assess their effectiveness in the investment decision-making process in the wind sector, more importantly, for Round 3 zones. The results are subject to the specifics of the underlying life cycle cost model.

Traditionally, the main focus of evaluation in universal electricity access problems has been cost. However, additional criteria such as increasing renewable penetration due to environmental concerns or grid penetration due to reliability concerns, have become increasingly important. We acknowledge the importance of additional criteria and propose a bi-objective framework so as to help decision makers investigate the trade-offs between potentially conflicting criteria in rural electrification. We consider two objective space based exact approaches using the Prize Collecting Steiner Tree (PCST) formulation and two metaheuristic algorithms to find Pareto solutions, and investigate their performances on real life problem instances. This study is expected to be an important decision support tool for the electrification of underdeveloped communities, having the potential of contributing to their socio-economic development.

RESUMEN: Se presenta un modelo de asignación óptima de presupuesto para mejoramiento de calidad del servicio en sistemas de distribución. El modelo consiste en un problema multiobjetivo que busca al mismo tiempo minimizar el costo de mantenimiento en sistemas de distribución y maximizar la reducción de la tasa de fallas. Este último objetivo se evalúa a través del indicador SAIFI (Frecuencia Media de Interrupción del Sistema). Para resolver el modelo propuesto se implementaron dos algoritmos poblacionales: Algoritmo Genético No-Dominado II (NSGA-II) y un Algoritmo Memético. Se realizan pruebas con dos sistemas eléctricos reales del Departamento de Antioquia en Colombia de 100 y 200 nodos, mostrando la aplicabilidad del modelo propuesto. Los frentes de Pareto óptimos obtenidos en la solución del problema muestran un set de posibles soluciones que representan un compromiso entre ambos objetivos y le dan al operador de red un estimado de cuánto debe invertir en mantenimiento para lograr un valor deseado del indicador SAIFI. ABSTRACT: This paper presents an optimal budget assignment model for improving quality service in distribution systems. The model consists on a multi objective problem which aims at minimizing maintenance costs in distribution systems while maximizing the reduction of faults rate. This last objective is measured through the SAIFI indicator (System Average Interruption Frequency Index). To solve the proposed model two algorithms were implemented: NSGAII (Non-Dominated Sorting Genetic Algorithm II) and a Memetic Algorithm. Several tests were performed with two real electrical systems of 100 and 200 nodes in the Department of Antioquia in Colombia, showing the applicability of the proposed approach. The optimal Pareto fronts obtained in the problem solution show a set of available options that represent a trade-off between both objectives and provides the system operator with an estimate of how much to invest in maintenance to achieve a desired value of the SAIFI indicator.

Best management practices (BMPs) are an effective way to control water pollution. However, identification of the optimal distribution and cost-effect of BMPs provides a great challenge for watershed policy makers. In this paper, a semi-distributed, low-data, and robust watershed model, the Revised Generalized Watershed Loading Function (RGWLF), is improved by adding the pollutant attenuation process in the river channel and a bank filter strips reduction function. Three types of pollution control measures&mdash, point source wastewater treatment, bank filter strips, and converting farmland to forest&mdash, are considered, and the cost of each measure is determined. Furthermore, the RGWLF watershed model is coupled with a widely recognized multi-objective optimization algorithm, the non-dominated sorting genetic algorithm II (NSGAII), the combination of which is applied in the Luanhe watershed to search for spatial BMPs for dissolved nitrogen (DisN). Fifty scenarios were finally selected from numerous possibilities and the results indicate that, at a minimum cost of 9.09 &times, 107 yuan, the DisN load is 3.1 &times, 107 kg and, at a maximum cost of 1.77 &times, 108 yuan, the total dissolved nitrogen load is 1.31 &times, 107 kg, with the no-measures scenario, the DisN load is 4.05 &times, 107 kg. This BMP optimization model system could assist decision-makers in determining a scientifically comprehensive plan to realize cost-effective goals for the watershed.

In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binary-real Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two control examples: a single input single output (SISO) system and a multivariable (MIMO) system.

[EN] The aim of this paper is to introduce a method to reduce the weight in structures which are subjected to multiple restrictions like deformation, max allowable stress, natural frequency, etc. The method is shown through the analysis of an aluminum bracket, whose maximum stress and deformation is well defined. The analysis is done using the Structural and Design of Experiments modules of Ansys Workbench v12.1. As result of the method a weight reduction of 50,2% is achieved. © (2012) Trans Tech Publications.

To cope up the network security measures with the financial restrictions in the corporate world is still a challenge. At global scenario the tradeoff between the protection of IT infrastructure and the financial boundation for any organization using IT as valuable resource is quite essential. Every organization has different security needs and different budgets for coping with that therefore whether it has to look as single objective or as multiple objectives with fault tolerant feature is a critical issue. In the present paper an attempt has been taken to optimize and analyze the effectiveness of security hardening measures considering attack tree model as base. In short we can say that the main attention in the paper is-to rectify, to describe the notations of the attack tree model and to suggest a model which may be able to quantitatively specify the possible threats as well as cost of the security control while implementing the security hardening measures. &nbsp