• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.495-504
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• 2013

Evolutionary algorithms have been applied to multi-objective optimization problems by approximation methods using computational intelligence. Those methods have been improved gradually in order to generate more exactly many approximate Pareto optimal solutions. The paper introduces a new method using support vector machine to find an approximate Pareto frontier in multi-objective optimization problems. Moreover, this paper applies an evolutionary algorithm to the proposed method in order to generate more exactly approximate Pareto frontiers. Then a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by the proposed method. Finally, a few examples will be demonstrated for the effectiveness of the proposed method.

• Journal of computational fluids engineering
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• v.14 no.4
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• pp.31-40
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• 2009

Although optimization by sequentially refining metamodels is known to be computationally very efficient, the metamodel that can be used for this purpose is limited to Kriging method due to the difficulties related with sample points selections. The present study suggests a novel method for sequentially refining metamodels using Pareto Frontiers, which can be used independent of the type of metamodels. It is shown from the examples that the present method yields more accurate metamodels compared with full-factorial optimization and also guarantees global optimum irrespective of the initial conditions. Finally, in order to prove the generality of the present method, it is applied to a 2D transonic airfoil optimization problem, and the successful design results are obtained.

• Journal of applied mathematics & informatics
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• v.5 no.1
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• pp.51-64
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• 1998

Conflict resolution methodology is discussed with fuzzified Pareto frontier. Four solution concepts namely the Nash solution the generalized nash solution the kalai-Smorodinsky concept and a solution method based on a special bargaining process are examined. The solutions are also fuzzy, the corresponding payoff values are fyzzy numbers the membership functions of which are determined. Three particular cases are considered in the paper. Linear quadratic, and general nonlinear pareto frontiers with known shape are examined.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.10 s.241
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• pp.1163-1171
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• 2005

The shape optimization of an airfoil has been performed for an incompressible viscous flow. In this study, Pareto frontier sets, which are global and non-dominated solutions, can be obtained without various weighting factors by using the multi-objective genetic algorithm An NACA0012 airfoil is considered as a baseline model, and the profile of the airfoil is parameterized and rebuilt with four Bezier curves. Two curves, front leading to maximum thickness, are composed of five control points and the rest, from maximum thickness to tailing edge, are composed of four control points. There are eighteen design variables and two objective functions such as the lift and drag coefficients. A generation is made up of forty-five individuals. After fifteenth evolutions, the Pareto individuals of twenty can be achieved. One Pareto, which is the best of the . reduction of the drag furce, improves its drag to $13\%$ and lift-drag ratio to $2\%$. Another Pareto, however, which is focused on increasing the lift force, can improve its lift force to $61\%$, while sustaining its drag force, compared to those of the baseline model.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.306-309
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• 2005

We consider the multi-objective optimization of a multi-service arrayed-waveguide grating-based single-hop metro WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. We develop and evaluate a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Our methodology provides the network architecture and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with our methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.2
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• pp.25-33
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• 2012

A genetic algorithm (GA) has been employed to optimize the major design variables of the liquid rocket engine. Pressure of the main combustion chamber, nozzle expansion ratio and O/F ratio have been selected as design variables. The target engine has the open gas generator cycle using the LO2/RP-1 propellant. The gas properties of the combustion chamber have been obtained from CEA2 and the mass has been estimated using reference data. The objective function has been set as multi-objective function with the specific impulse and thrust to weight ratio using the weight method. The result shows about 4% improvement of the specific impulse and 23% increase of the thrust to weight ratio. The Pareto frontier line has been also obtained for various thrust requirements.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.127-134
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• 2011

A genetic algorithm (GA) has been employed to optimize the major design variables of the liquid rocket engine. Pressure of the main combustion chamber, nozzle expansion ratio and O/F ratio have been selected as design variables. The target engine has the open gas generator cycle using the LO2/RP-1 propellant. The gas properties of the combustion chamber have been obtained from CEA2 and the mass has been estimated using reference data. The objective function has been set as multi-objective function with the specific impulse and thrust to weight ratio using the weight method. The result shows about 4% improvement of the specific impulse and 23% increase of the thrust to weight ratio. The Pareto frontier line has been also obtained for various thrust requirements.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.151-160
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• 2013

In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality non-dominated solutions.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.7
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• pp.500-511
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• 2007

The Optimal solutions of the design variables in internally finned tubes have been obtained for three-dimensional periodically fully developed turbulent flow and heat transfer. For a trapezoidal fin profile, performances of the heat exchanger are determined by considering the heat transfer rate and pressure drop, simultaneously, that are interdependent quantities. Therefore, Pareto frontier sets of a heat exchanger can be acquired by integrating CFD and a multi-objective optimization technique. The optimal values of fin widths $(d_1,\;d_2)$, fin height(h) and helix angle$(\gamma)$ are numerical1y obtained by minimizing the pressure loss and maximizing the heat transfer rate within ranges of $d_1=0.5\sim1.5mm$, $d_2=0.5\sim1.5mm$, $h=0.5\sim1.5mm$, and $\gamma=0\sim20^{\circ}$. For this, a general CFD code and a global genetic algorithm(GA) are used. The Pareto sets of the optimal solutions can be acquired after $30^{th}$ generation.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.39-45
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• 2008

This paper considers the multi-objective optimization of a multi-service arrayed waveguide grating-based single-hop WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. This paper presents a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Genetic algorithm based methodology provides the network architecture parameters and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with this methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.