• Title/Summary/Keyword: Pareto 해

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Development of Pareto-Optimal Technique for Generation Planning According to Environmental Characteristics in term (환경특성을 반영한 급전계획의 파레토 최적화기법 개발)

  • Lee, Buhm;Kim, Yong-ha;Choi, Sang-kyu
    • Journal of Energy Engineering
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    • v.13 no.2
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    • pp.128-132
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    • 2004
  • This paper presents a new methodology to get pareto-optimal solution for generation planning. First, we apply dynamic programming, and we can get an optimal economic dispatch considering total quantity of contamination for the specified term. Second, we developed a method which can get pareto-optimal solution. This solution is consisted of a set of optimal generation planning. As a result, decision maker can get pareto-optimal solutions, and can choose a solution. We applied this method to the test system, and showed the usefulness.

Goal-Pareto based NSGA Optimization Algorithm (Goal-Pareto 기반의 NSGA 최적화 알고리즘)

  • Park, Jun-Su;Park, Soon-Kyu;Shin, Yo-An;Yoo, Myung-Sik;Lee, Won-Cheol
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.44 no.2 s.314
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    • pp.108-115
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    • 2007
  • This paper proposes a new optimization algorithm prescribed by GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm) whose result satisfies the user's needs and goals to enhance the performance of optimization. Typically, lots of real-world engineering problems encounter simultaneous optimization subject to satisfying prescribed multiple objectives. Unfortunately, since these objectives might be mutually competitive, it is hardly to find a unique solution satisfying every objectives. Instead, many researches have been investigated in order to obtain an optimal solution with sacrificing more than one objectives. This paper introduces a novel optimization scheme named by GBNSGA obeying both goals as well as objectives as possible as it can via allocating candidated solutions on Pareto front, which enhances the performance of Pareto based optimization. The performance of the proposed GBNSGA will be compared with that of the conventional NSGA and weighted-sum approach.

Evolutionary Multi-Objective Optimization Algorithms for Uniform Distributed Pareto Optimal Solutions (균일분포의 파레토 최적해 생성을 위한 다목적 최적화 진화 알고리즘)

  • Jang Su-Hyun;Yoon Byungjoo
    • The KIPS Transactions:PartB
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    • v.11B no.7 s.96
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    • pp.841-848
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    • 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.

Study on Diversity of Population in Game model based Co-evolutionary Algorithm for Multiobjective optimization (다목적 함수 최적화를 위한 게임 모델에 기반한 공진화 알고리즘에서의 해집단의 다양성에 관한 연구)

  • Lee, Hea-Jae;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.7
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    • pp.869-874
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    • 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.

A study on the optimal design of rope way (索道線路의 最適設計에 대한 硏究)

  • 최선호;박용수
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.11 no.1
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    • pp.26-35
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    • 1987
  • As an attempt to make the multi-objection for the line design of the rope way, the resulted formulas from the catenary curve as exact ones were summarized, and it was found out that the Kuhn-Tucker's optimality conditions and regions of the objective functions can analytically be expressed with dimensionless parameters. The Pareto's optimum solution set was analytically obtained through the objective function-the minimum relation of $W^{*}$, and $W^{*}$ is a trade-off relation. From this, The dimension of a rope and the value of an initial tension that are the standard in design of the rope way were determined. It was concluded that $V^{*}$ should become minimum, and that the ratio of the dimension of rope to the value of and initial tension become larger than superposition factor corresponding to curve AB.to curve AB.

Development of a Multiobjective Optimization Algorithm Using Data Distribution Characteristics (데이터 분포특성을 이용한 다목적함수 최적화 알고리즘 개발)

  • Hwang, In-Jin;Park, Gyung-Jin
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.34 no.12
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    • pp.1793-1803
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    • 2010
  • The weighting method and goal programming require weighting factors or target values to obtain a Pareto optimal solution. However, it is difficult to define these parameters, and a Pareto solution is not guaranteed when the choice of the parameters is incorrect. Recently, the Mahalanobis Taguchi System (MTS) has been introduced to minimize the Mahalanobis distance (MD). However, the MTS method cannot obtain a Pareto optimal solution. We propose a function called the skewed Mahalanobis distance (SMD) to obtain a Pareto optimal solution while retaining the advantages of the MD. The SMD is a new distance scale that multiplies the skewed value of a design point by the MD. The weighting factors are automatically reflected when the SMD is calculated. The SMD always gives a unique Pareto optimal solution. To verify the efficiency of the SMD, we present two numerical examples and show that the SMD can obtain a unique Pareto optimal solution without any additional information.

Robust parameter set selection of unsteady flow model using Pareto optimums and minimax regret approach (파레토 최적화와 최소최대 후회도 방법을 이용한 부정류 계산모형의 안정적인 매개변수 추정)

  • Li, Li;Chung, Eun-Sung;Jun, Kyung Soo
    • Journal of Korea Water Resources Association
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    • v.50 no.3
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    • pp.191-200
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    • 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.

Generating of Pareto frontiers using machine learning (기계학습을 이용한 파레토 프런티어의 생성)

  • Yun, Yeboon;Jung, Nayoung;Yoon, Min
    • 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.

Ranking the Pareto-optimal Solutions using DEA-based Ranking Procedure: an Application to Multi-reservoir Operation Problem (DEA기반 순위결정 절차를 이용한 파레토 최적해의 우선순위 결정: 저수지군 연계 운영문제를 중심으로)

  • Jeon, Seung-Mok;Kim, Jae-Hee;Kim, Sheung-Kown
    • IE interfaces
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    • v.21 no.1
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    • pp.75-84
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    • 2008
  • It is a difficult task for decision makers(DMs) to choose an appropriate release plan which balances the conflicts between water storage and hydro-electric energy generation in a multi-reservoir operation problem. In this study, we proposed a DEA-based ranking procedure by which the DM can rank the potential alternatives and select the best solution among the Pareto-optimal solutions. The proposed procedure can resolve the problem of mix inefficiency that may cause errors in measuring the efficiency of alternatives. We applied the proposed procedure to the multi-reservoir operation problem for the Geum-River basin and could choose the best efficient solution from the Pareto-set which were generated by the Coordinated Multi-Reservoir Operating Model.

Automatic Calibration of Rainfall-runoff Model Using Multi-objective Function (다중목적함수를 이용한 강우-유출 모형의 자동보정)

  • Lee, Kil-Seong;Kim, Sang-Ug;Hong, Il-Pyo
    • Journal of Korea Water Resources Association
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    • v.38 no.10 s.159
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    • pp.861-869
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    • 2005
  • A rainfall-runoff model should be calibrated so that the model simulates the hydrological behavior of the basin as accurately as possible. In this study, to calibrate the five parameters of the SSARR model, a multi-objective function and the genetic algorithm were used. The solution of the multi-objective function will not, in general, be a single unique set of parameters but will consist of the so-called Pareto solution according to various trade-offs between the different objectives. The calibration strategy using multi-objective function could decrease calibrating time and effort. From the Pareto solution, a single solution could be selected to simulate a specific flow condition.