• 한국지능시스템학회논문지
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• 제21권6호
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• pp.686-691
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• 2011

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

• 한국수자원학회논문집
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• 제52권10호
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• pp.681-695
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• 2019

Transient Stroage Model (TSM)은 하천을 본류대와 저장대로 나누어 각각에 대한 오염물의 혼합거동을 해석함으로써 복잡한 하천에 유입된 오염물질 혼합을 이해하는 데에 가장 많이 이용되는 모형 중 하나이다. TSM의 매개변수들은 역산모형을 통해 산정하게 되는데 이는 자연하천에서 추적자실험을 통해 계측된 농도곡선에 가장 잘 맞는 TSM 모의 농도곡선을 찾는 최적화 문제이다. 저장대모형의 매개변수 산정에 관한 선행 연구들에 의해 매개변수를 산정하는 최적화 문제의 비볼록(non-convex) 특성에서 오는 불확실성이 보고되어 왔다. 본 연구에서는 청미천에서 수행된 추적자실험으로부터 취득된 농도곡선을 이용해 최상의 최적화 기법과 목적함수의 조합에 대해 분석하였다. 최적화 문제의 수렴성과 수렴 속도를 모두 만족하는 최적화 조건을 결정하기 위해 SCE-UA의 CCE와 SP-UCI의 MCCE와 같은 진화 알고리즘 기반의 전역 최적화 방법들과 오차 기반 목적함수들을 Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL)을 활용해 비교하였다. 전반적인 변수 산정 결과 여러 EA를 동시에 적용한 SC-SAHEL을 평균 제곱오차를 목적함수로 한 방법이 가장 빠르고 가장 안정적으로 최적해에 수렴하는 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권4호
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권5호
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• pp.241-248
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• 2000

Until now, all of the port goods are transported manually by container transporter in the port. Recently there are a lot of studies about unmanned vehicle driven automatically. In terms of the vehicle automation, the control of steering and velocity on vehicle systems is very important part in container transporter. In common sense, vehicle systems have lots of nonlinear parameters so we have many difficulties in designing the optimal controller of them. In this paper, we present a design of the TDOF PID controller using a hybrid schematic algorithm to control the steering system optimally. We used the single-track model to pre-test the designed controller before appling to AGV. We also used the ES(evolutionary strategy) and SA(simulated annealing) algorithms to construct the hybrid tuning algorithm for parameters of controller. Finally, we had the computer simulation to verify that our designed controller has better performance than the other one.

• Industrial Engineering and Management Systems
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• 제11권4호
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• 대한전기학회논문지:전력기술부문A
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• 제50권12호
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• pp.584-590
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• 2001

This paper presents a windows program package for solving security constrained OPF in interconnected Power systems, which is based on the combined application of evolutionary programming(EP) and sequential quadratic programming(SQP). The objective functions are the minimization of generation fuel costs and system power losses. The control variables are the active power of the generating units, the voltage magnitude of the generator, transformer tap settings and SYC setting. The state variables are the bus voltage magnitude, the reactive power of the generating unit, line flows and the tie line flow In OPF considering security, the outages are selected by contingency ranking method. The resulting optimal operating point has to be feasible after outages such as any single line outage(respect of voltage magnitude, reactive power generation and power flow limits). The OPF package proposed is applied to IEEE 14 buses and 10 machines 39 buses model system.

• 한국산학기술학회논문지
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• 제19권4호
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• pp.683-688
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• 2018

최근 하이브리드 전기자동차의 보급 확대에 따라 전기에너지 수요가 증가하고 있다. 본 연구에서는 전기에너지 수요에 대응하기 위해 에너지 하베스팅 기술을 이용해 자가발전이 가능한 3가지 구조의 현가장치 적용 선형 발전 시스템을 ANSYS MAXWELL을 사용하여 전자기 시뮬레이션을 통해 각 구조의 발전 특성을 비교 분석 하였다. 다음으로 각 모델에 대해 상용 PIDO(Process Integration and Design Optimization)툴인 PIAnO(Process Integration, Automation and Optimization)을 사용하여 최적설계를 수행하였다. 3가지 설계변수를 선정하여 실험계획법 기법 중 직교 배열표(Orthogonal Array)를 이용해 도출한 18개의 실험 점에 대해 전자기 해석을 통해 완성한 실험계획법을 바탕으로 근사 모델을 생성하였으며 진화 알고리즘(Evolutionary Algorithm)을 이용한 최적 설계를 수행하였다. 마지막으로 초기 모델과 동일한 해석 조건을 사용해 최적 설계 결과 모델에 대한 전자기 시뮬레이션을 통해 최적설계 결과를 검증 하였다. 각 선형 발전기 모델에 대해 최적의 구조에 대한 발전 특성을 비교한 결과 8pole-8slot, 12pole-12slot, 16pole-16slot 구조에서 최대 발전량은 각각 366.5W, 466.7W, 579.7W로 slot, pole 조합 수가 많아질수록 발전량이 증가하는 결과를 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• Journal of Electrical Engineering and Technology
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• 제7권4호
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• pp.530-537
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• 2012

In the recent past, genetic algorithm (GA) and evolutionary optimization scheme have become increasingly popular for the design of electromagnetic (EM) devices. However, the conventional GA suffers from computational drawback and parameter dependency when applied to a computationally expensive problem, such as practical EM optimization design. To overcome these issues, a hybrid optimization scheme using GA in conjunction with Kriging is proposed. The algorithm is validated by using two mathematical problems and by optimizing rotor structure of interior permanent magnet synchronous machine.