• Title, Summary, Keyword: Differential Evolution

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Design of Optimized Fuzzy Controller for Rotary Inverted Pendulum System Using Differential Evolution (차분진화 알고리즘을 이용한 회전형 역 진자 시스템의 최적 퍼지 제어기 설계)

  • Kim, Hyun-Ki;Lee, Dong-Jin;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.2
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    • pp.407-415
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    • 2011
  • In this study, we propose the design of optimized fuzzy controller for the rotary inverted pendulum system by using differential evolution algorithm. The structure of the differential evolution algorithm has a simple structure and its convergence to optimal values is superb in comparison to other optimization algorithms. Also the differential evolution algorithm is easier to use because it have simpler mathematical operators and have much less computational time when compared with other optimization algorithms. The rotary inverted pendulum system is nonlinear and has a unstable motion. The objective is to control the position of the rotating arm and to make the pendulum to maintain the unstable equilibrium point at vertical position. The output performance of the proposed fuzzy controller is considered from the viewpoint of performance criteria such as overshoot, steady-state error, and settling time through simulation and practical experiment. From the result of both simulation and practical experiment, we evaluate and analyze the performance of the proposed optimal fuzzy controller from the comparison between PGAs and differential evolution algorithms. Also we show the superiority of the output performance as well as the characteristic of differential evolution algorithm.

Application of Opposition-based Differential Evolution Algorithm to Generation Expansion Planning Problem

  • Karthikeyan, K.;Kannan, S.;Baskar, S.;Thangaraj, C.
    • Journal of Electrical Engineering and Technology
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    • v.8 no.4
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    • pp.686-693
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    • 2013
  • Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Opposition-based Differential Evolution (ODE) algorithms have been applied to the GEP problem. The original GEP problem has been modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units have been considered. The results have been compared with Dynamic Programming (DP) method. The ODE performs well and converges faster than DE.

Design of Fuzzy Models with the Aid of an Improved Differential Evolution (개선된 미분 진화 알고리즘에 의한 퍼지 모델의 설계)

  • Kim, Hyun-Ki;Oh, Sung-Kwun
    • Journal of Korean Institute of Intelligent Systems
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    • v.22 no.4
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    • pp.399-404
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    • 2012
  • Evolutionary algorithms such as genetic algorithm (GA) have been proven their effectiveness when applying to the design of fuzzy models. However, it tends to suffer from computationally expensWive due to the slow convergence speed. In this study, we propose an approach to develop fuzzy models by means of an improved differential evolution (IDE) to overcome this limitation. The improved differential evolution (IDE) is realized by means of an orthogonal approach and differential evolution. With the invoking orthogonal method, the IDE can search the solution space more efficiently. In the design of fuzzy models, we concern two mechanisms, namely structure identification and parameter estimation. The structure identification is supported by the IDE and C-Means while the parameter estimation is realized via IDE and a standard least square error method. Experimental studies demonstrate that the proposed model leads to improved performance. The proposed model is also contrasted with the quality of some fuzzy models already reported in the literature.

Observation of Bargaining Game using Co-evolution between Particle Swarm Optimization and Differential Evolution (입자군집최적화와 차분진화알고리즘 간의 공진화를 활용한 교섭게임 관찰)

  • Lee, Sangwook
    • The Journal of the Korea Contents Association
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    • v.14 no.11
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    • pp.549-557
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    • 2014
  • Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

CONTROLLABILITY RESULTS FOR IMPULSIVE NEUTRAL EVOLUTION DIFFERENTIAL SYSTEMS

  • Selbi, S.;Arjunan, M. Mallika
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.16 no.2
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    • pp.93-105
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    • 2012
  • In this paper, we consider the controllability of a certain class of impulsive neutral evolution differential equations in Banach spaces. Sufficient conditions for controllability are obtained by using the Hausdorff measure of noncompactness and Monch fixed point theorem under the assumption of noncompactness of the evolution system.

Global Optimization Using Differential Evolution Algorithm (차분진화 알고리듬을 이용한 전역최적화)

  • Jung, Jae-Joon;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.11
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    • pp.1809-1814
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    • 2003
  • Differential evolution (DE) algorithm is presented and applied to global optimization in this research. DE suggested initially fur the solution to Chebychev polynomial fitting problem is similar to genetic algorithm(GA) including crossover, mutation and selection process. However, differential evolution algorithm is simpler than GA because it uses a vector concept in populating process. And DE turns out to be converged faster than CA, since it employs the difference information as pseudo-sensitivity In this paper, a trial vector and its control parameters of DE are examined and unconstrained optimization problems of highly nonlinear multimodal functions are demonstrated. To illustrate the efficiency of DE, convergence rates and robustness of global optimization algorithms are compared with those of simple GA.

Identifying Temporal Pattern Clusters to Predict Events in Time Series

  • Heesoo Hwang
    • KIEE International Transaction on Systems and Control
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    • v.2D no.2
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    • pp.125-134
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    • 2002
  • This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.

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Thermal Unit Commitment Using Binary Differential Evolution

  • Jeong, Yun-Won;Lee, Woo-Nam;Kim, Hyun-Houng;Park, Jong-Bae;Shin, Joong-Rin
    • Journal of Electrical Engineering and Technology
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    • v.4 no.3
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    • pp.323-329
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    • 2009
  • This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary optimization problems such as UC problems since it is restricted to continuous-valued spaces. This paper proposes binary differential evolution (BDE), which enables the DE to operate in binary spaces and applies the proposed BDE to UC problems. Furthermore, this paper includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in UC problems. Since excessive spinning reserves can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BDE, it is applied to largescale power systems of up to 100-units with a 24-hour demand horizon.

Differential Evolution Algorithm for Job Shop Scheduling Problem

  • Wisittipanich, Warisa;Kachitvichyanukul, Voratas
    • Industrial Engineering and Management Systems
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    • v.10 no.3
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    • pp.203-208
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    • 2011
  • Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.