• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.340-353
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• 2010

Properly designed and well-executed services enable e-commerce companies to capture the unique needs and preferences of customers, help them build customer loyalty, and thereby, strengthen their competitiveness in the marketplace. An object of this study is the new open-market company and it has only 3 percent of the market share. Therefore, it is needed to raise the market share by improving the service qualities. This article presents a six sigma project for service quality improvement in the open-market company. This study was carried out based on five steps of DMAIC which is six sigma technique. First, a defect rate was defined as unsatisfaction rate. In addition, 50-people data was analyzed and it was shown that the defect level was 2.5 sigma level. In this study, in order to raise the sigma level, novel eight action items were determined based on SIPOC, FDPM, cause and effect diagram, matrix chart, Pareto chart and statistical analysis.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.56-66
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• 1998

This paper investigates a neuro-controller combined in parallel with a conventional linear controller of PID type in order to control nonminimum phase systems more efficiently. The objective is to minimize overall position errors as well as to maintain small undershooting. A costfunction is proposed with two conflict objectives. The neuro-controller is trained off-line with evolutionary programming(EP) in such a way that it becomes optimal by minimizing the given cost function through global evaluation based on desired control performance during the whole training time interval. However, it is not easy to find an optimal solution which satisfies individual objective simultaneously. With the concept of Pareto optimality and EP, we train the proposed controller more effectively and obtain a valuable set of optimal solutions. Simulation results show the efficacy of the proposed controller in a viewpoint of improvement of performance of a step response like fast settling time and small undershoot or overshoot compared with that of a conventional linear controller.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.325-337
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• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.245-253
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• 1998

Recently, the importance of multi-objective optimization techniques and stochastic search methods is increasing. The stochastic search methods have the concepts of the survival of the fittest and natural selection such as genetic algorithms(GA), simulated annealing(SA) and evolution strategies (ES). As many accidents of oil tankers cause marine pollution, oil tankers of double hull or mid deck structure are being built to minimize the marine pollution. For the improvement of oil tanker design technique, an efficient optimization technique is proposed in this study. Multi-objective optimization problem of weight and cost of double hull and mid deck tanker is formulated. Discrete design variables are used considering real manufacturing, and the concept of relative production cost is also introduced. The ES method is used as an optimization technique, and the ES algorithm was developed to generate a more efficient Pareto optimal set.

• IE interfaces
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• v.13 no.4
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• pp.688-693
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• 2000

We analyze IS0 9001 audit data and add-on requirements data collected by leader assessors from three leading Korean furniture companies for around three years. We plot a Pareto chart and test the homogeneities for the number of non-compliances and improvement notes across companies. We also fit the data to a loglinear model. Some recommendations with regard to add-on requirements are suggested. The recommendations should be added to IS0 9001 requirements to specifically implement an efficient QMS in Korean furniture industry.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.203-211
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• 2022

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

• Journal of Korea Technology Innovation Society
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• v.17 no.4
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• pp.733-762
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• 2014

The government mandates that national research facilities & equipments through R&D business budget should be registered on the National Science and Technology Information Service (NTIS) for the purpose of the efficient use of the research facilities & equipments. This study is to contribute to the national policies on the efficient management of the research facilities & equipments by recognition methodology with the university's members and analysis of the impact factors of the universities' registration process improvement through the Define level and Measure level of the Six Sigma DAMIC. The survey and interview were conducted on research directors, professors joining university administration, graduate students, researchers, and staffs of A University. The findings are the lack of understanding specific steps and life-cycle management of research facilities & equipments. It is necessary to collect suggestions from universities and pursue policies considered the unique characteristics of the university for advanced operating and maximizing use of university's national research facilities & equipments. Research facilities & equipments enrollment compliance rate and registration accuracy were selected as CTQ-Y through the Six Sigma. 72 potential cause variables were derived through Process Map and C & E Diagram. 13 variables were determined as core potential factors through the X-Y Matrix and Pareto Chart. Research institutions should maximize utilization of research facilities & equipments through deriving a potential variables of the process improvements and designing a detail improvements based on the characteristics of each institutions.