• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.491-496
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• 2002

Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.677-685
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• 2007

The objective of this study is to evaluate the applicability of multi-objective genetic algorithm(MOGA) in order to calibrate the parameters of conceptual rainfall-runoff model, Tank model. NSGA-II, one of the most imitating MOGA implementations, is combined with Tank model and four multi-objective functions such as to minimize volume error, root mean square error (RMSE), high flow RMSE, and low flow RMSE are used. When NSGA-II is employed with more than three multi-objective functions, a number of Pareto-optimal solutions usually becomes too large. Therefore, selecting several preferred Pareto-optimal solutions is essential for stakeholder, and preference-ordering approach is used in this study for the sake of getting the best preferred Pareto-optimal solutions. Sensitivity analysis is performed to examine the effect of initial genetic parameters, which are generation number and Population size, to the performance of NSGA-II for searching the proper paramters for Tank model, and the result suggests that the generation number is 900 and the population size is 1000 for this study.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.314-322
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• 2007

This paper proposes a new optimization algorithm named by GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm) which determines the best configuration for CR(Cognitive Radio) communication systems. Conventionally, in order to select the proper radio configuration, genetic algorithm has been introduced so as to alleviate computational burden along the execution of the cognition cycle proposed by Mitola. This paper proposes a novel optimization algorithm designated as GBNSGA for cognitive engine which can be described as a hybrid algorithm combining well-known Pareto-based NSGA(Non-dominated Sorting Genetic Algorithm) as well as GP(Goal Programming). By conducting computer simulations, it will be verified that the proposed method not only satisfies the user's service requirements in the form of goals. It reveals the fast optimization capability and more various solutions rather than conventional NSGA or weighted-sum approach.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.893-904
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• 2013

In this paper, a Kriging metamodel-based multi-objective optimization strategy in conjunction with an NSGA-II(non-dominated sorted genetic algorithm-II) has been employed to optimize the valve-plate shape of the axial piston pump utilizing 3D CFD simulations. The optimization process for minimum pressure ripple and maximum pump efficiency is composed of two steps; (1) CFD simulation of the piston pump operation with various combination of six parameters selected based on the optimization principle, and (2) applying a multi-objective optimization approach based on the NSGA-II using the CFD data set to evaluate the Pareto front. Our exploration shows that we can choose an optimal trade-off solution combination to reach a target efficiency of the axial piston pump with minimum pressure ripple.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1106-1110
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• 2010

This paper proposes a method of optimizing a stacking strategy for an automated container terminal using multi-objective evolutionary algorithms (MOEAs). Since the yard productivities of seaside and landside are conflicting objectives to be optimized, it is impossible to maximize them simultaneously. Therefore, we derive a Pareto optimal set instead of a single best solution using an MOEA. Preliminary experiments showed that the population is frequently stuck in local optima because of the difficulty of the given problem depending on the yard occupancy rate. To cope with this problem, we propose another method of simultaneously optimizing two problems with different difficulties so that diverse solutions can be preserved in the population. Experimental results showed the proposed method can derive better stacking policies than the compared method solving a single problem given the same computational costs.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.20-33
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• 2006

Because of rapid expansion of third party logistics, fierce competition in the transportation industry, and the diversification and globalization of transportation channels, an effective transportation planning by means of multimodal transport is badly needed. Accordingly, this study aims to suggest an optimal transport algorithm for the multimodal transport in the international logistics. As a solution for this problem, first of all, we have applied a pruning algorithm to simplify it, suggesting a heuristic algorithm for constrained shortest path problem to find out a feasible area with an effective time range, which has been applied to the Label Setting Algorithm, consequently leading to multiple Pareto optimal solutions. Also, in order to test the efficiency of the algorithm for constrained shortest path problem, this paper has applied it to the actual transportation path from Busan port of Korea to Rotterdam port of Netherlands.

• Journal of Korea Water Resources Association
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• v.41 no.12
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• pp.1211-1218
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• 2008

This study is for design of the detention system distributed in a watershed by the Multi-Objective Genetic Algorithms(MOGAs). A new model is developed to determine optimal size and location of detention. The developed model has two primary interfaced components such as a rainfall runoff model to simulate water surface elevation(or flowrate) and MOGAs to get the optimal solution. The objective functions used in this model depend on the peak flow and storage of detention. With various constraints such as structural limitations, capacities of storage and operational targets. The developed model is applied at Gwanyang basin within Anyang watershed. The simulation results show the maximum outlet reduction is occurred at detention facilities located in upper reach of watershed in the peak discharge rates. It is also reviewed the simultaneous construction of an off-line detention and an on-line detention. The methodologies obtained from this study will be used to control the flood discharges and to reduce flood damage in urbanized watershed.

• Journal of Korean Association for Spatial Structures
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• v.10 no.4
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• pp.93-102
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• 2010

In this study, a smart sky-bridge composed of MR damper and FPS has been proposed and vibration control performance of a smart sky-bridge for the connected buildings was investigated. To this end, 10-story and 20-story building structures connected by a smart sky-bridge were selected as example structures and El Centro and Kobe earthquakes, which have near and far fault ground motion characteristics respectively, were used for time history analyses. In order to effectively control the smart sky-bridge, fuzzy logic controller was developed and multi-objective genetic algorithm was used to optimize fuzzy logic controllers. Based on optimization results, it has been seen that there is a trade-off between seismic responses of 10-story and 20-story buildings and a suite of Pareto optimal solutions of fuzzy logic controllers for seismic response control can be obtained by multi-objective genetic algorithm. It is shown from numerical study that seismic responses of adjacent buildings can be efficiently controlled by using a smart sky-bridge.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.351-356
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• 2022

In this paper, we present the regression analysis and design optimization for improving the permeability of 3D woven materials based on numerical analysis data. First, the parametric analysis model is generated with variables that define the gap sizes between each directional wire of the woven material. Then, material properties such as bulk modulus, thermal conductivity coefficient, and permeability are calculated using numerical analysis, and these material data are used in the polynomial-based regression analysis. The Pareto optimal solution is obtained between bulk modulus and permeability by using multi-objective optimization and shows their trade-off relation. In addition, gradient-based design optimization is applied to maximize the fluid permeability for 3D woven materials, and the optimal designs are obtained according to the various minimum bulk modulus constraints. Finally, the optimal solutions from regression equations are verified to demonstrate the accuracy of the proposed method.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.95-108
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• 2006

Because of rapid expansion of third party logistics, fierce competition in the transportation industry, and the diversification and globalization of transportation channels, an effective transportation planning by means of multimodal transport is badly needed. Accordingly, this study aims to suggest an optimal transport algorithm for the multimodal transport in the international logistics. Cargoes and stopovers can be changed numerously according to the change of transportation modes, thus being a NP-hard problem. As a solution for this problem, first of all, we have applied a pruning algorithm to simplify it, suggesting a heuristic algorithm for constrained shortest path problem to find out a feasible area with an effective time range and effective cost range, which has been applied to the Label Setting Algorithm, consequently leading to multiple Pareto optimal solutions. Also, in order to test the efficiency of the algorithm for constrained shortest path problem, this paper has applied it to the actual transportation path from Busan port of Korea to Rotterdam port of Netherlands.