• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.113-116
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• 2008

This work presents numerical optimization for design of staggered arrays of dimples printed on opposite surfaces of a cooling channel with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by three non-dimensional geometric design variables composed of dimpled channel height, dimple print diameter, dimple spacing and dimple depth to maximize heat transfer rate compromising with pressure drop. Twenty designs generated by Latin hypercube sampling were evaluated by Reynolds-averaged Navier-Stokes solver and the evaluated objectives were used to construct Pareto optimal front through hybrid multi-objective evolutionary algorithm. The optimum designs were grouped by k-mean clustering technique and some of the clustered points were evaluated by flow analysis. With increase in dimple depth, heat transfer rate increases and at the same time pressure drop also increases, while opposite behavior is obtained for the dimple spacing. The heat transfer performance is related to the vertical motion of the flow and the reattachment length in the dimple.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.179-186
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• 2005

This paper describes a genetic algorithm and compares three crossover operators for Rural Postman Problem with Time Windows (RPPTW). The RPPTW which is a multiobjective optimization problem, is an extension of Rural Postman Problem(RPP) in which some service places (located at edge) require service time windows that consist of earliest time and latest time. Hence, RPM is a m띤tieect optimization Problem that has minimal routing cost being serviced within the given time at each service Place. To solve the RPPTW which is a multiobjective optimization problem, we obtain a Pareto-optimal set that the superiority of each objective can not be compared. This Paper performs experiments using three crossovers for 12 randomly generated test problems and compares the results. The crossovers using in this Paper are Partially Matched Exchange(PMX) Order Exchange(OX), and Modified Order Exchange(MOX) which is proposed in this paper. For each test problem, the results show the efficacy of MOX method for RPPTW.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.539-550
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• 2005

The spanning tree, which is being used the most widely in indoor wiring network, is chosen for the network topology of the optimal LAN design. To apply a spanning tree to GA, the concept of $Pr\ddot{u}fer$ numbers is used. $Pr\ddot{u}fer$ numbers can express he spanning tree in an efficient and brief way, and also can properly represent the characteristics of spanning trees. This paper uses Pareto Stratum-Niche Cubicle(PS-NC) GA by complementing the defect of the same priority allowance in non-dominated solutions of pareto genetic algorithm(PGA). By applying the PS-NC GA to the LAN design areas, the optimal LAN topology design in terms of minimizing both message delay time and connection-cost could be accomplished in a relatively short time. Numerical analysis has been done for a hypothetical data set. The results show that the proposed algorithm could provide better or good solutions for the multi-objective LAN design problem in a fairly short time.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.29-46
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• 2007

This paper makes an attempt to compare the two important methods for finding solutions of multi-item inventory problem with more than one conflicting objectives. Panda et al.[9] discusses a distance-based method to find the best possible compromise solution with variation of priority under the given weight structure. In this paper, the problem in [9] is revisited through the Pareto-optimal front of genetic algorithm with the help of a situation of retail stocking of FMCG business. The advantages of using the solutions from the perspective of the decision maker obtained through multi-objective optimization are highlighted in terms of population search, weighted goals and priority structure, cost, set of compromise solutions along with prevention of stock-out situation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1071-1076
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• 2003

Evolutionary algorithm is recognized as a promising approach to solving multi-objective combinatorial optimization problems. When no preference information of decision makers is given, multi-objective optimization problems have been commonly used to search for diverse and good Pareto optimal solution. In this paper we propose a new multi-objective evolutionary algorithm based on competitive coevolutionary algorithm, and demonstrate the applicability of the algorithm. The proposed algorithm is designed to promote both population diversity and rapidity of convergence. To achieve this, the strategies of fitness evaluation and the operation of the Pareto set are developed. The algorithm is applied to job shop scheduling problems (JSPs). The JSPs have two objectives: minimizing makespan and minimizing earliness or tardiness. The proposed algorithm is compared with existing evolutionary algorithms in terms of solution quality and diversity. The experimental results reveal the effectiveness of our approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.3
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• pp.25-43
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• 2010

We consider a multi-objective balancing and sequencing problem in mixed model assembly lines, which is important for an efficient use of the assembly lines. In this paper, we present a neighborhood symbiotic evolutionary algorithm to simultaneously solve the two problems of balancing and model sequencing under multiple objectives. We aim to find a set of well-distributed solutions close to the true Pareto optimal solutions for decision makers. The proposed algorithm has a two-leveled structure. At Level 1, two populations are operated : One consists of individuals each of which represents a partial solution to the balancing problem and the other consists of individuals for the sequencing problem. Level 2, which is an upper level, works one population whose individuals represent the combined entire solutions to the two problems. The process of Level 1 imitates a neighborhood symbiotic evolution and that of Level 2 simulates an endosymbiotic evolution together with an elitist strategy to promote the capability of solution search. The performance of the proposed algorithm is compared with those of the existing algorithms in convergence, diversity and computation time of nondominated solutions. The experimental results show that the proposed algorithm is superior to the compared algorithms in all the three performance measures.

• IE interfaces
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• v.24 no.3
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• pp.220-230
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• 2011

The multi-reservoir operation problem for efficient utilization of water resources involves conflicting objectives, and the problem can be solved by varying weight coefficient on objective functions. Accordingly, decision makers need to choose appropriate weight coefficients balancing the trade-offs among multiple objectives. Although the appropriateness of the weight coefficients may depend on the total amount of water inflow, reservoir operating policy may not be changed to a certain degree for different hydrological conditions on inflow. Therefore, we propose to use fuzzy Data Envelopment Analysis (DEA) to rank the weight coefficients in consideration of the inflow variation. In this approach, we generate a set of Paretooptimal solutions by applying different weight coefficients on Coordinated Multi-reservoir Operating Model. Then, we rank the Pareto-optimal solutions or the corresponding weight coefficients by using Fuzzy DEA model. With the proposed approach, we can suggest the best weight coefficients that can produce the appropriate Pareto-optimal solution considering the uncertainty of inflow, whereas the general DEA model cannot pinpoint the best weight coefficients.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.77-91
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• 2007

In this paper, we present a symbiotic evolutionary algorithm for multi-objective optimization. The goal in multi-objective evolutionary algorithms (MOEAs) is to find a set of well-distributed solutions close to the true Pareto optimal solutions. Most of the existing MOEAs operate one population that consists of individuals representing the entire solution to the problem. The proposed algorithm has a two-leveled structure. The structure is intended to improve the capability of searching diverse and food solutions. At the lower level there exist several populations, each of which represents a partial solution to the entire problem, and at the upper level there is one population whose individuals represent the entire solutions to the problem. The parallel search with partial solutions at the lower level and the Integrated search with entire solutions at the upper level are carried out simultaneously. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The optimization problems with continuous variables and discrete variables are used as test-bed problems. The experimental results confirm the effectiveness of the proposed algorithm.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.10
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• pp.938-946
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• 2005

Shape optimization of internally finned circular tube has been peformed for periodically fully developed turbulent flow and heat transfer. The physical domain considered in this study is very complicated due to periodic boundary conditions both streamwise and circumferential directions. Therefore, Pareto frontier sets of a heat exchanger can be acquired by coupling the CFD and the multi-objective genetic algorithm, which is a global optimization technique. The optimal values of fin widths $(d_1,\;d_2)$ and fin height (H) are numerically obtained by minimizing the pressure loss and maximizing the heat transfer rate within ranges of $d_1=0.2\sim1.5\;mm,\;d_2=0.2\sun1.5\;mm,\;and\;H=0.2\sim1.5\;mm$. The optimal values of the design variables are acquired after the fifth generation and also compared to those of a local optimization algorithm for the same geometry and conditions.

• Steel and Composite Structures
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• v.7 no.1
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• pp.53-70
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• 2007

This paper presents a analysis of the problem of optimal design of the beams with two I-type cross section shapes. These types of beams are simply supported and subject to pure bending. The strength and stability conditions were formulated and analytically solved in the form of mathematical equations. Both global and selected types of local stability forms were taken into account. The optimization problem was defined as bicriteria. The cross section area of the beam is the first objective function, while the deflection of the beam is the second. The geometric parameters of cross section were selected as the design variables. The set of constraints includes global and local stability conditions, the strength condition, and technological and constructional requirements in the form of geometric relations. The optimization problem was formulated and solved with the help of the Pareto concept of optimality. During the numerical calculations a set of optimal compromise solutions was generated. The numerical procedures include discrete and continuous sets of the design variables. Results of numerical analysis are presented in the form of tables, cross section outlines and diagrams. Results are discussed at the end of the work. These results may be useful for designers in optimal designing of thin-walled beams, increasing information required in the decision-making procedure.