• Journal of Energy Engineering
• /
• v.13 no.2
• /
• pp.128-132
• /
• 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.

• 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.

• Journal of the Society of Naval Architects of Korea
• /
• v.45 no.2
• /
• pp.151-156
• /
• 2008

It is necessary to perform the research works on the general structural designs and optimum structural designs of double hull tankers and bulk carriers due to the newly built Common Structural Rules(CSR). In this study, an optimum structural design of a mid-ship part of double hull oil tanker was carried out by using the CSR. An optimum structural design program was developed by using the Pareto optimal based multi-objective function method. The hull weight and fabrication cost obtained by the single and multi-objective function methods were compared with existing ship by the consideration of CSR and material cost which is recently increasing.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.6
• /
• pp.987-992
• /
• 2002

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. In this paper, pareto-based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. This algorithm is based on Continuous Evolutionary Algorithms to solve single objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche-formation method fur fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto-optimal tradeoff surface. Finally, the validity of this method has been demonstrated through a numerical example.

• Journal of Industrial Convergence
• /
• v.18 no.3
• /
• pp.19-25
• /
• 2020

In the software development process, software reliability evaluation is a very important issue. In particular, finding the optimal development model that satisfies high reliability is the more important task for software developers. For this, in this study, Pareto and Erlang life distributions were applied to the finite failure NHPP model to evaluate the reliability attributes. For this purpose, parametric estimation is applied to the maximum likelihood estimation method, and nonlinear equations are calculated using the bisection method. As a result, the Erlang model showed better performance than the Pareto model in the evaluation of the strength function and the mean value function. Also, as a result of inputting future mission time and evaluating reliability, the Erlang model showed an effectively high trend together with the Pareto model, while the Goel-Okumoto basic model showed a decreasing trend. In conclusion, the Erlang model is the best model among the proposed models. Through this study, it is expected that software developers will be able to use it as a basic guideline for exploring and evaluating the optimal software reliability model.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.2 s.314
• /
• pp.108-115
• /
• 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.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.11 no.3
• /
• pp.49-56
• /
• 2011

This paper deals with a methodology for searching optimal route of hazard material (hazmat) vehicles. When we make a decision of hazmat optimal paths, there is a conflict between the public aspect which wants to minimize risk and the private aspect which has a goal of minimizing travel time. This paper presents Efficient Vector Labeling algorithm as a methodology for searching optimal path of hazmat transportation, which is intrinsically one of the multi-criteria decision making problems. The output of the presented algorithm is a set of Pareto optimal paths considering both risk and travel time at a time. Also, the proposed algorithm is able to identify non-dominated paths which are significantly different from each other in terms of links used. The proposed Efficient Vector Labeling algorithm are applied to test bed network and compared with the existing k-shortest path algorithm. Analysis of result shows that the proposed algorithm is more efficient and advantageous in searching reasonable alternative routes than the existing one.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.22 no.1
• /
• pp.100-106
• /
• 2013

This paper describes an integrated optimization design using multilevel decomposition technique on the base of the parametric distribution and independent axiom at the stages of lower level. Based on Pareto optimum solution, the detailed parameters at the lower level can be defined into the independent axiom. The suspension design is used as the simulation example.

• Journal of the Society of Naval Architects of Korea
• /
• v.45 no.2
• /
• pp.221-228
• /
• 2008

This paper describes to determine optimum main particulars of warships which satisfy user's requirements in a concept design stage with minimum construction cost and maximum transportation efficiency. Present worth was used as an assessment criteria of the economical efficiency. And Pareto optimal set was used to have the optimum design.