• Clean Technology
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• v.7 no.2
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• pp.99-107
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• 2001

Optimization methodologies have been proposed of find the best environment-friendly recycling pathways of plastic materials based on life-cycle assessment (LCA) methodology. The main difficulty in conducting this optimization study is that multiple environmental burdens have to be considered simultaneously as the cost functions. Instead of generating conservative Pareto or noninferior solutions following multi-objective optimization approaches, we have proposed some practical criteria on how to combine the different environmental burdens into a single measure. The obtained single objective optimization problem can then be solved by conventional nonlinear programming techniques or, more effectively, by a tree search method based on decision flows. The latter method reduces multi-dimensional optimization problems to a set of one-dimensional problems in series. It is expected the suggested tree search approach can be applied to many LCA studies as a new promising optimization tool.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.43-48
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• 2003

In this study the optimization of plate-fin type heat sink for the thermal stability is performed numerically. The optimum design variables are obtained when the temperature rise and the pressure drop are minimized simultaneously. The flow and thermal fields are predicted using the finite volume method and the optimization is carried out by using the sequential quadratic programming (SQP) method which is widely used in the constrained nonlinear optimization problem. The results show that when the temperature rise is less than 34.6 K, the optimal design variables are as follows; $B_{1}$ = 2.468 mm, $B_{2}$ = 1.365 mm, and t = 10.962 mm. The Pareto optimal solutions are also presented for the pressure drop and the temperature rise.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.325-337
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• 2012

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.165-175
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• 2015

One of the current challenges in the domain of the multicriteria shape optimization is to reduce the calculation time required by conventional methods. The high computational cost is due to the high number of simulation or function calls required by these methods. Recently, several studies have been led to overcome this problem by integrating a metamodel in the overall optimization loop. In this paper, we perform a coupling between the Normal Boundary Intersection - NBI - algorithm with Radial Basis Function - RBF - metamodel in order to have a simple tool with a reasonable calculation time to solve multicriteria optimization problems. First, we apply our approach to academic test cases. Then, we validate our method against an industrial case, namely, shape optimization of the bottom of an aerosol can undergoing nonlinear elasto-plastic deformation. Then, in order to select solutions among the Pareto efficient ones, we use the same surrogate approach to implement a method to compute Nash and Kalai-Smorodinsky equilibria.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.3
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• pp.293-302
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• 2004

In this study the optimization of plate-fin type heat sink for the thermal stability is peformed numerically. The optimum design variables are obtained when the temperature rise and the pressure drop are minimized simultaneously. The flow and thermal fields are predicted using the finite volume method and the optimization is carried out by using the sequential quadratic programming (SQP) method which is widely used in the constrained non-linear optimization problem. The results show that when the temperature rise is less than 34.6K, the optimal design variables are as follows; B$_1$=2.468mm, B$_2$=1.365mm, and t=10.962mm. The Pareto optimal solutions are also presented for the pressure drop and the temperature rise.

• International Journal of High-Rise Buildings
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• v.10 no.1
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• pp.35-43
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• 2021

With the rapid urbanization and growing energy use intensity in the built environment, the glazed curtainwall has become ever more important in the architectural practice and environmental stewardship. Besides its energy efficiency roles, window has been an important transparent component for daylight penetration and a view-out for occupant satisfaction. In response to the climate crisis caused by the built environment, this research focuses on the study of net-zero energy retrofitting by using a new building integrated photovoltaic (BIPV) curtainwall as a sustainable alternative to conventional window systems. Design variables such as building orientations, climate zones, energy attributes of BIPV curtainwalls, and glazed area were studied, to minimize energy consumption and discomfort hours for three cities representing hot (Miami, FL), mixed (Charlotte, NC), and cold (Minneapolis, MN). Parametric analysis and Pareto solutions are presented to provide a comprehensive explanation of the correlation between design variables and performance objectives for net-zero energy retrofitting applications.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.203-211
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• 2022

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

• Wind and Structures
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• v.34 no.3
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• pp.259-274
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• 2022

The present paper is focused on analyzing a set of Computational Fluid Dynamics (CFD) simulation data on reducing orthogonal peak base moment coefficients on a high-rise rectangular building with wings. The study adopts an aerodynamic optimization procedure (AOP) composed of CFD, artificial neural network (ANN), and genetic algorithm (G.A.). A parametric study is primarily accomplished by altering the wing positions with 3D transient CFD analysis using k - ε turbulence models. The CFD technique is validated by taking up a wind tunnel test. The required design parameters are obtained at each design point and used for training ANN. The trained ANN models are used as surrogates to conduct optimization studies using G.A. Two single-objective optimizations are performed to minimize the peak base moment coefficients in the individual directions. An additional multiobjective optimization is implemented with the motivation of diminishing the two orthogonal peak base moments concurrently. Pareto-optimal solutions specifying the preferred building shapes are offered.

• Journal of Wetlands Research
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• v.26 no.1
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• pp.10-20
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• 2024

The conservation and restoration of wetlands are essential tasks for the sustainable development of human society and the environment, providing vital benefits such as biodiversity maintenance, natural disaster mitigation, and climate change alleviation. This study aims to analyze the strategic interactions and interests among various stakeholders using game theory and to provide significant grounds for policy decisions related to wetland restoration and development. In this study, hypothetical scenarios were set up for three types of cities: large, medium, and small. Stakeholders such as governments, development companies, environmental groups, and local residents were identified. Strategic options for each stakeholder were developed, and a payoff matrix was established through discussions among wetland ecology experts. Subsequently, non-cooperative game theory was applied to analyze Nash equilibria and Pareto efficiency. In large cities, strategies of 'Wetland Conservation' and 'Eco-Friendly Development' were found beneficial for all stakeholders. In medium cities, various strategies were identified, while in small cities, 'Eco-Friendly Development' emerged as the optimal solution for all parties involved. The Pareto efficiency analysis revealed how the optimal solutions for wetland management could vary across different city types. The study highlighted the importance of wetland conservation, eco-friendly development, and wetland restoration projects for each city type. Accordingly, policymakers should establish regulations and incentives that harmonize environmental protection and urban development and consider programs that promote community participation. Understanding the roles and strategies of stakeholders and the advantages and disadvantages of each strategy is crucial for making more effective policy decisions.

• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.351-358
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• 2013

This paper proposes a multiobjective model describing the trade-offs involved in selecting the locations of drop-off boxes for collecting used products and transporting these products to designated locations. We assume the following reverse flow of used products. Owners of used products (cellular phones, digital cameras, ink cartridges, etc.) take them to the nearest drop-off box when the distance is reasonably short. We also assume that owners living closer to drop-off boxes dispose of more used products than do owners living farther from drop-off boxes. Different types of used products are collected, with each type requiring its own drop-off box. A transportation destination for each product is specified. Three objectives are considered: maximizing the volume of used products collected at drop-off boxes; minimizing the cost of transporting collected products to designated locations; and minimizing the cost of allocating space for drop-off boxes. We formulate the above model as a multiobjective integer programming problem and generate the corresponding set of Pareto optimal solutions. We apply the model to an area using population data for Chofu City, Tokyo, Japan, and analyze the trade-offs between the objectives.