• 제목/요약/키워드: Intelligent optimization methods

검색결과 139건 처리시간 0.036초

지능형 최적화 기법 이용한 하이브리드 자기부상 시스템의 설계 (Design of Hybrid Magnetic Levitation System using Intellignet Optimization Algorithm)

  • 조재훈;김용태
    • 전기학회논문지
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    • 제66권12호
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    • pp.1782-1791
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    • 2017
  • In this paper, an optimal design of hybrid magnetic levitation(Maglev) system using intelligent optimization algorithms is proposed. The proposed maglev system adopts hybrid suspension system with permanent-magnet(PM) and electro magnet(EM) to reduce the suspension power loss and the teaching-learning based optimization(TLBO) that can overcome the drawbacks of conventional intelligent optimization algorithm is used. To obtain the mathematical model of hybrid suspension system, the magnetic equivalent circuit including leakage fluxes are used. Also, design restrictions such as cross section areas of PM and EM, the maximum length of PM, magnetic force are considered to choose the optimal parameters by intelligent optimization algorithm. To meet desired suspension power and lower power loss, the multi object function is proposed. To verify the proposed object function and intelligent optimization algorithms, we analyze the performance using the mean value and standard error of 10 simulation results. The simulation results show that the proposed method is more effective than conventional optimization methods.

개선된 수업-학습기반 최적화 알고리즘을 이용한 자기부상 제어기의 최적 설계 (Optimal Design of Magnetic Levitation Controller Using Advanced Teaching-Learning Based Optimization)

  • 조재훈;김용태
    • 전기학회논문지
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    • 제64권1호
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    • pp.90-98
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    • 2015
  • In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.

Modern Probabilistic Machine Learning and Control Methods for Portfolio Optimization

  • Park, Jooyoung;Lim, Jungdong;Lee, Wonbu;Ji, Seunghyun;Sung, Keehoon;Park, Kyungwook
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제14권2호
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    • pp.73-83
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    • 2014
  • Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

Fuzzy-Enforced Complementarity Constraints in Nonlinear Interior Point Method-Based Optimization

  • Song, Hwachang
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제13권3호
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    • pp.171-177
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    • 2013
  • This paper presents a fuzzy set method to enforce complementarity constraints (CCs) in a nonlinear interior point method (NIPM)-based optimization. NIPM is a Newton-type approach to nonlinear programming problems, but it adopts log-barrier functions to deal with the obstacle of managing inequality constraints. The fuzzy-enforcement method has been implemented for CCs, which can be incorporated in optimization problems for real-world applications. In this paper, numerical simulations that apply this method to power system optimal power flow problems are included.

다봉성 함수의 최적화를 위한 향상된 유전알고리듬의 제안 (An Enhanced Genetic Algorithm for Optimization of Multimodal Function)

  • 김영찬;양보석
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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    • pp.241-244
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    • 2000
  • The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide resemblance between individuals and research optimum solutions by single point method in reconstructive research space. Two numerical examples are also presented in this paper to comparing with conventional methods.

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A Parallel Genetic Algorithms with Diversity Controlled Migration and its Applicability to Multimodal Function Optimization

  • YAMAMOTO, Fujio;ARAKI, Tomoyuki
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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    • pp.629-633
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    • 1998
  • Proposed here is a parallel genetic algorithm accompanied with intermittent migration among subpopulations. It is intended to maintain diversity in the population for a long period . This method was applied to finding out the global maximum of some multimodal functions for which no other methods seem to be useful . Preferable results and their detailed analysis are also presented.

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바이어스항이 있는 GBAM 모델을 이용한 양방향 연상메모리 구현 (Implementation of Bidirectional Associative Memories Using the GBAM Model with Bias Terms)

  • 임채환;박주영
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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    • pp.69-72
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    • 2001
  • In this paper, we propose a new design method for bidirectional associative memories model with high error correction ratio. We extend the conventional GBAM model using bias terms and formulate a design procedure in the form of a constrained optimization problem. The constrained optimization problem is then transformed into a GEVP(generalized eigenvalue problem), which can be efficiently solved by recently developed interior point methods. The effectiveness of the proposed approach is illustrated by a example.

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비선형제한조건을 갖는 최적화문제 신경회로망 (Neural Networks for Optimization Problem with Nonlinear Constraints)

  • 강민제
    • 한국지능시스템학회논문지
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    • 제12권1호
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    • pp.1-6
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    • 2002
  • Hopfield는 선형 제한조건을 갖는 선형프로그램밍을 풀 수 있는 신경회로망을 제안하였는 데, 이 논문에서는 제한조건함수가 비선형함수를 포함하는 일반적인 최적화문제를 해결할 수 있는 신경망으로 확장하였다. 또한, 최적화문제를 신경회로망에 매핑시키는 방법, 그리고 회로로 구성하는 방법들이 논의되었다.

Robust Algorithms for Combining Multiple Term Weighting Vectors for Document Classification

  • Kim, Minyoung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제16권2호
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    • pp.81-86
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    • 2016
  • Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

Designing New Algorithms Using Genetic Programming

  • Kim, Jin-Hwa
    • 한국지능정보시스템학회:학술대회논문집
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    • 한국지능정보시스템학회 2004년도 추계학술대회
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    • pp.171-178
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    • 2004
  • This study suggests a general paradigm enhancing genetic mutability. Mutability among heterogeneous members in a genetic population has been a major problem in application of genetic programming to diverse business problems. This suggested paradigm is implemented to developing new methods from existing methods. Within the evolutionary approach taken to designing new methods, a general representation scheme of the genetic programming framework, called a kernel, is introduced. The kernel is derived from the literature of algorithms and heuristics for combinatorial optimization problems. The commonality and differences among these methods have been identified and again combined by following the genetic inheritance merging them. The kernel was tested for selected methods in combinatorial optimization. It not only duplicates the methods in the literature, it also confirms that each of the possible solutions from the genetic mutation is in a valid form, a running program. This evolutionary method suggests diverse hybrid methods in the form of complete programs through evolutionary processes. It finally summarizes its findings from genetic simulation with insight.

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