• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.401-404
• /
• 2004

진화 알고리즘은 여러 개의 상충하는 목적을 갖는 다목적 최적화 문제를 해결하기에 적합한 방법이다. 특히, 파레토 지배관계에 기초하여 개체의 적합도를 평가하는 파레토 기반 진화알고리즘들은 그 성능에 있어서 우수한 평가를 받고 있다. 최근의 파레토 기반 진화알고리즘들은 전체 파레토 프론트에 균일하게 분포하는 해집합의 생성을 위해 개체들의 밀도를 개체의 적합도를 평가하기 위한 하나의 요소로 사용하고 있다. 그러나 밀도의 역할은 전체 진화과정에서 중요한 요소가 되기보다는 파레토 프론트에 어느 정도 수렴된 후, 개체의 균일 분포를 만들기 위해 사용된다. 본 논문에서 우리는 파레토 지배 순위와 밀도에 대한 적응적가중치를 이용한 다목적 최적화 진화알고리즘을 제안한다. 제안한 알고리즘은 진화 개체의 적합도를 평가하기위해 파레토 순위와 밀도에 대한 적응적 가중치를 적용하여 전체 진화과정에서 파레토 순위와 밀도가 전체 진화 개체집합의 상태를 고려하여 영향을 미치도록 하였다. 제안한 방법을 많은 지역해들을 포함하는 ZDT4문제에 적용한 결과 비교적 우수한 수렴 결과를 보였다.

• The KIPS Transactions:PartB
• /
• v.11B no.7 s.96
• /
• pp.841-848
• /
• 2004

Evolutionary a1gorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uni-formly distributed onto Pareto optimal front. In this paper, we propose an evolutionary a1gorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms Published. Seed individual means a solution which is not located in the crowded region on Pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed a1go-rithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algo-rithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-modal problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.11a
• /
• pp.104-107
• /
• 2007

다목적 함수의 최적화 문제(Multiobjective optimization problems)의 경우에는 하나의 최적해가 존재하는 것이 아니라 '파레토 최적해 집합(Pareto optimal set)'이라고 알려진 해들의 집합이 존재한다. 이러한 이상적 파레토 최적해 집합과 가까운 최적해를 찾기 위한 다양한 해탐색 능력은 진화 알고리즘의 성능을 결정한다. 본 논문에서는 게임 모텔에 기반한 공진화 알고리즘(GCEA:Game model based Co-Evolutionary Algorithm)에서 해집단의 다양성을 유지하여, 다양한 비지배적 파레토 대안해(non-dominated alternatives)들을 찾기 위한 방법을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.7
• /
• pp.869-874
• /
• 2007

In searching for solutions to multiobjective optimization problem, we find that there is no single optimal solution but rather a set of solutions known as 'Pareto optimal set'. To find approximation of ideal pareto optimal set, search capability of diverse individuals at population space can determine the performance of evolutionary algorithms. This paper propose the method to maintain population diversify and to find non-dominated alternatives in Game model based Co-Evolutionary Algorithm.

• Journal of Korean Society of Transportation
• /
• v.18 no.3
• /
• pp.77-86
• /
• 2000

The shortest path problem is one of the mathematical Programming models that can be conveniently solved through the use of networks. The common shortest Path Problem is to minimize a single objective function such as distance, time or cost between two specified nodes in a transportation network. The sing1e objective model is not sufficient to reflect any Practical Problem with multiple conflicting objectives in the real world applications. In this paper, we consider the shortest Path Problem under multiple objective environment. Wile the shortest path problem with single objective is solvable in Polynomial time, the shortest Path Problem with multiple objectives is NP-complete. A genetic a1gorithm approach is developed to deal with this Problem. The results of the experimental investigation of the effectiveness of the algorithm are also Presented.

• The KIPS Transactions:PartB
• /
• v.11B no.2
• /
• pp.213-220
• /
• 2004

Evolutionary algorithms are well-suited for multi-objective optimization problems involving several. often conflicting objective. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. Recently, pareto-based evolutionary algorithms uses a density information in fitness assignment scheme for generating uniform distributed global pareto optimal front. However, the usage of density information is not Important elements in a whole evolution path but plays an auxiliary role in order to make uniform distribution. In this paper, we propose an evolutionary algorithms for multi-objective optimization which assigns the fitness using pareto dominance rank and density weighting, and thus pareto dominance rank and density have similar influence on the whole evolution path. Furthermore, the experimental results, which applied our method to the six multi-objective optimization problems, show that the proposed algorithms show more promising results.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.3
• /
• pp.495-504
• /
• 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 Korean Society of Steel Construction
• /
• v.14 no.3
• /
• pp.395-402
• /
• 2002

This study proposed a muti-objective optimum design method for rational optimizing of orthotropic steel deck bridges. This multi-objective optimum design method was found to be effective in optimizing multi-objective problems, considering cost and deflection functions. It may ve difficult to optimize orthotropic steel deck bridges using a conventional optimization, since the bridges have several parts and show complex structural behaviors. Therefore, the Pareto curve can be obtained by performing the multi-objective optimization for real orthotropic steel deck bridges, using the multi-level technique with excellent efficiency. A reasonable and economical design can be attained using the Parato curve in the cost and deflection functions of the bridge. Thus, more reasonable design values can be determined based on a comparison with those using a conventional design procedure.

• The Journal of Information Technology and Database
• /
• v.5 no.1
• /
• pp.1-12
• /
• 1998

분산 환경에서 데이터의 할당(allocation)는 중요한 설계 이슈이다. 데이터의 할당은 분산 데이터에 대한 비용(cost) 감소, 성능(performance) 및 가용성(availability) 향상 등의 이점을 극대화할 수 있도록 최적화되어야 한다. 기존 연구들의 대부분은 트랜잭션의 수행 비용을 최소화하는 방향으로만 최적화된 데이터 할당 결과를 제시하고 있다. 즉, 비용, 성능 및 가용성을 모두 함께 고려하는 연구는 아직까지 제시된 결과가 없으며 이는 복잡한 모델에 대한 적절한 최적화 기법이 없기 때문이다. 본 연구에서는 분산 데이터의 이점들인 비용, 성능 및 가용성 등의 다중측면을 동시에 고려함으로써 데이터 할당에 대한 파레토 최적해를 제공하는 DAMMA (Data Allocation Methodology considering Multiple Aspects) 방법론을 제안하였다. DAMMA 방법론은 데이터 분할 과정을 통하여 생성된 최적의 단편들을 분산 시스템의 운용 비용, 수행 성능, 가용성 등의 요소를 고려하여 각 물리적 사이트에 중복 할당하는 파레토 최적해들을 생성해낼 수 있는 설계 방법론이다.

• Journal of Korea Water Resources Association
• /
• v.50 no.3
• /
• pp.191-200
• /
• 2017

A robust parameter set (ROPS) selection framework for an unsteady flow model was developed by combining Pareto optimums obtained by outcomes of model calibration using multi-site observations with the minimax regret approach (MRA). The multi-site calibration problem which is a multi-objective problem was solved by using an aggregation approach which aggregates the weighted criteria related to different sites into one measure, and then performs a large number of individual optimization runs with different weight combinations to obtain Pareto solutions. Roughness parameter structure which can describe the variation of Manning's n with discharges and sub-reaches was proposed and the related coefficients were optimized as model parameters. By applying the MRA which is a decision criterion, the Pareto solutions were ranked based on the obtained regrets related to each Pareto solution, and the top-rated one due to the lowest aggregated regrets of both calibration and validation was determined as the only ROPS. It was found that the determination of variable roughness and the corresponding standardized RMSEs at the two gauging stations varies considerably depending on the combinations of weights on the two sites. This method can provide the robust parameter set for the multi-site calibration problems in hydrologic and hydraulic models.