• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.10
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• pp.860-869
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• 2003

Design optimization of the heat sink with 7${\times}$7 square pin-fins is performed numerically using the Computational Fluid Dynamics (CFD) and the Computer Aided Optimization (CAO). In the pin-fins heat sink, the optimum design variables for fin height (h), fin width (w), and fan-to-heat sink distance (c) can be achieved when the thermal resistance ($\theta$$_{j}$) at the junction and the overall pressure drop ($\Delta$p) are minimized simultaneously. To complete the optimization, the finite volume method for calculating the objective functions, the BFGS method for solving the unconstrained non-linear optimization problem, and the weighting method for predicting the multi-objective problem are used. The results show that the optimum design variable for the weighting coefficient of 0.5 are as follows: w=4.653 mm, h=59.215 mm, and c=2.667 mm. In this case, the objective functions are predicted as 0.56K/W of thermal resistance and 6.91 Pa of pressure drop. The Pareto optimal solutions are also presented.re also presented.d.

• Wind and Structures
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• v.30 no.4
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• pp.379-392
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• 2020

The paper presents a Life-Cycle Cost-based optimization framework for wind-excited tall buildings equipped with Tuned Mass Dampers (TMDs). The objective is to minimize the Life-Cycle Cost that comprises initial costs of the structure, the control system and costs related to repair, maintenance and downtime over the building's lifetime. The integrated optimization of structural sections and mass ratio of the TMDs is carried out, leading to a set of Pareto optimal solutions. The main advantage of the proposed methodology is that, differently from the traditional optimal design approach, it allows to perform the unified design of both the structure and the control system in a Life Cycle Cost Analysis framework. The procedure quantifies wind-induced losses, related to structural and nonstructural damage, considering the stochastic nature of the loads (wind velocity and direction), the specificity of the structural modeling (e.g., non-shear-type vibration modes and torsional effects) and the presence of the TMDs. Both serviceability and ultimate limit states related to the structure and the TMDs' damage are adopted for the computation of repair costs. The application to a case study tall building allows to demonstrate the efficiency of the procedure for the integrated design of the structure and the control system.

• Smart Structures and Systems
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• v.16 no.1
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• Earthquakes and Structures
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• v.7 no.3
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• pp.271-294
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• 2014

In recent years, along with the advances made in performance-based design optimization, the need for fast calculation of response parameters in dynamic analysis procedures has become an important issue. The main problem in this field is the extremely high computational demand of time-history analyses which may convert the solution algorithm to illogical ones. Two simplifying strategies have shown to be very effective in tackling this problem; first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication, second, wavelet analysis of earthquake records decreasing the number of acceleration points involved in time-history loading. In this paper, we try to develop an efficient framework, using both strategies, to solve the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency (FEMA) recommended design specifications. The results from numerical application of the proposed framework demonstrate the capabilities of the framework in solving the present multi-objective optimization problem.

• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4329-4348
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• 2019

Mobile cloud computing (MCC) can offload heavy computation from mobile devices onto nearby cloudlets or remote cloud to improve the performance as well as to save energy for these devices. Therefore, it is essential to consider how to achieve efficient computation offloading with constraints for multiple users. However, there are few works that aim at multi-objective problem for multiple users. Most existing works concentrate on only single objective optimization or aim to obtain a tradeoff solution for multiple objectives by simply setting weight values. In this paper, a multi-objective optimization model is built to minimize the average energy consumption, time and cost while satisfying the constraint of bandwidth. Furthermore, an improved multi-objective optimization algorithm called D-NSGA-II-ELS is presented to get Pareto solutions with better convergence and diversity. Compared to other existing works, the simulation results show that the proposed algorithm can achieve better performance in terms of energy consumption, time and cost while satisfying the constraint of the bandwidth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2976-2990
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• 2022

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.677-689
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• 2022

Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGA-II). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.

• Steel and Composite Structures
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• v.51 no.6
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• pp.647-659
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• 2024

Given the increased interest in enhancing structural sustainability, the current study sought to apply multiobjective optimization to a footbridge with a steel-concrete composite I-girder structure. It was considered as objectives minimizing the cost for building the structure, the environmental impact assessed by CO2 emissions, and the vertical accelerations created by human-induced vibrations, with the goal of ensuring pedestrian comfort. Spans ranging from 15 to 25 meters were investigated. The resistance of the slab's concrete, the thickness of the slab, the dimensions of the welded steel I-profile, and the composite beam interaction degree were all evaluated as design variables. The optimization problem was handled using the Multiobjective Harmony Search (MOHS) metaheuristic algorithm. The optimization results were used to generate a Pareto front for each span, allowing us to assess the correlations between different objectives. By evaluating the values of design variables in relation to different levels of pedestrian comfort, it was identified optimal values that can be employed as a starting point in predimensioning of the type of structure analyzed. Based on the findings analysis, it is possible to highlight the relationship between the structure's cost and CO2 emission objectives, indicating that cost-effective solutions are also environmentally efficient. Pedestrian comfort improvement is especially feasible in smaller spans and from a medium to a maximum level of comfort, but it becomes expensive for larger spans or for increasing comfort from minimum to medium level.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.9
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• pp.557-567
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• 2016

In this study, the optimal design of heat exchangers, including the evaporator and condenser of a solar-heating ocean thermal energy conversion (SH-OTEC), is investigated. The power output of the SH-OTEC is assumed to be 100 kW, and the SH-OTEC uses the working fluid of R134a and high-performance commercial tubes. The surface heat transfer area and the pressure drop were strongly dependent on the number of tubes, as well as the number of tube passes. To solve the reciprocal tendency between the heat transfer area and pressure drop with respect to the number of tubes, as well as the number of tube passes, a genetic algorithm (GA) with two objective functions of the heat transfer area (the capital cost) and operating cost (pressure drop) was used. Optimal results delineated the feasible regions of heat transfer area and operating cost with respect to the pertinent number of tubes and tube passes. Pareto fronts of the evaporator and condenser obtained from multi-objective GA provides designers or investors with a wide range of optimal solutions so that they can select projects suitable for their financial resources. In addition, the surface heat transfer area of the condenser took up a much higher percentage of the total heat transfer area of the SH-OTEC than that of the evaporator.