• Structural Engineering and Mechanics
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• v.26 no.4
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• pp.459-477
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• 2007

A bi-directional tuned mass damper (BTMD) in which a mass connected by two translational springs and two viscous dampers in two orthogonal directions has been introduced to control coupled lateral and torsional vibrations of asymmetric building. An efficient control strategy has been presented in this context to control displacements as well as acceleration responses of asymmetric buildings having asymmetry in both plan and elevation. The building is idealized as a simplified 3D model with two translational and a rotational degrees of freedom for each floor. The principles of rigid body transformation have been incorporated to account for eccentricity between center of mass and center of rigidity. The effective and robust design of BTMD for controlling the vibrations in structures has been presented. The redundancy of optimum design has been checked. Non dominated sorting genetic algorithm (NSGA) has been used for tuning optimum stages and locations of BTMDs and its parameters for control of vibration of seismically excited buildings. The optimal locations have been observed to be reasonably compact and practically implementable.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.3
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• pp.246-255
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• 2017

The proposed design methodology represents a new approach to optimize the propeller-hull system simultaneously. In this paper, two objective functions are considered, the first objective function is Lifetime Fuel Consumption (LFC) and the other one is cost function including thrust, torque, open water and skew efficiencies. The variables of the propeller geometries (Z, EAR, P/D and D) and ship hull parameters (L/B, B/T, T and $C_B$) are considered to be optimized with cavitation, blades stress of propeller. The well-known evolutionary algorithm based on NSGA-II is employed to optimize a multi-objective problem, where the main propeller and hull dimensions are considered as design variables. The results are presented for a series 60 ship with B-series propeller. The results showed that the proposed method is an appropriate and effective approach for simultaneously propeller-hull system design and is able to minimize both of the objective functions significantly.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.256-265
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• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

• Structural Engineering and Mechanics
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• v.50 no.2
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• pp.151-161
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• 2014

In this paper, fuel tank of the locomotive ER 24 has been studied. Firstly the behavior of fuel and air during the braking time has been investigated by using a two-phase model. Then, the distribution of pressure on the surface of baffles caused by sloshing has been extracted. Also, the fuel tank has been modeled and analyzed using Finite Element Method (FEM) considering loading conditions suggested by the DIN EN 12663 standard and real boundary conditions. In each loading condition, high stressed areas have been identified. By comparing the distribution of pressure caused by sloshing phenomena and suggested loading conditions, optimization of the tank has been taken into consideration. Moreover, internal baffles have been investigated and by modifying their geometric properties, search of the design space has been done to reach the optimal tank. Then, in order to reduce the mass and manufacturing cost of the fuel tank, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Artificial Neural Networks (ANNs) have been employed. It is shown that compared to the primary design, the optimized fuel tank not only provides the safety conditions, but also reduces mass and manufacturing cost by %39 and %73, respectively.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.451-458
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• 2006

The goal of many researchers in the field of structural engineering is to reduce both damage to building structures and discomfort of their inhabitants during strong motion seismic events. The present paper reports on analytical work conducted with this aim in mind as a prior research of experimental study. A four-story, 6.4 m tall, laboratory model of a building is employed as a example structure. The laboratory structure has graphite epoxy columns and each floor is equipped with a chevron brace that serves to resist inter-story drift with the installation of a magnetorheological (MR) damper. An artificial excitation has been generated with a robust range of seismic characteristics. A series of numerical simulations demonstrates that an optimized fuzzy controller is capable of robust performance for a variety of seismic base motions. Optimization of the fuzzy controller is achieved using multi-objective genetic algorithm(MOGA), i.e. NSGA-II. Multiple objective functions are used in order to reduce both peak and root-means-squared displacement and accelerations at the floor levels of the building.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.713-724
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• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3293-3311
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• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.4
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• pp.254-262
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• 2011

This study aims to optimize an arrangement of ship compartments with knowledge-based systems. Though great attention has been shown to the optimization of hull forms in recent years, the study on arrangement design optimization has received relatively little attention. A ship is both an engineering system and a kind of assembly of many spaces. This means that, to design an arrangement of ship compartments, it is necessary to treat not only geometric data but also knowledge on topological relations between spaces and components of a ship. In this regard, we select a suitable knowledge representation scheme for describing ship compartments and their relations, and then develop a knowledge-based system using expert system shell. This new approach is applied to create design variations for optimization on an arrangement of a pressure hull of a submerged vehicle. Finally, we explicate how our approach improves the design process.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.6
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.