• Title/Summary/Keyword: differential evolution.

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Differential Evolution Algorithm based on Random Key Representation for Traveling Salesman Problems (외판원 문제를 위한 난수 키 표현법 기반 차분 진화 알고리즘)

  • Lee, Sangwook
    • The Journal of the Korea Contents Association
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    • v.20 no.11
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    • pp.636-643
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    • 2020
  • The differential evolution algorithm is one of the meta-heuristic techniques developed to solve the real optimization problem, which is a continuous problem space. In this study, in order to use the differential evolution algorithm to solve the traveling salesman problem, which is a discontinuous problem space, a random key representation method is applied to the differential evolution algorithm. The differential evolution algorithm searches for a real space and uses the order of the indexes of the solutions sorted in ascending order as the order of city visits to find the fitness. As a result of experimentation by applying it to the benchmark traveling salesman problems which are provided in TSPLIB, it was confirmed that the proposed differential evolution algorithm based on the random key representation method has the potential to solve the traveling salesman problems.

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.

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 the 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.

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.

Cooperative Coevolution Differential Evolution Based on Spark for Large-Scale Optimization Problems

  • Tan, Xujie;Lee, Hyun-Ae;Shin, Seong-Yoon
    • Journal of information and communication convergence engineering
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    • v.19 no.3
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    • pp.155-160
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    • 2021
  • Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

Large Scale Cooperative Coevolution Differential Evolution (대규모 협동진화 차등진화)

  • Shin, Seong-Yoon;Tan, Xujie;Shin, Kwang-Seong;Lee, Hyun-Chang
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.665-666
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    • 2022
  • Differential evolution is an efficient algorithm for continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems quickly degrades performance and exponentially increases runtime. To overcome this problem, a new cooperative coevolution differential evolution based on Spark (referred to as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

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Cooperative Coevolution Differential Evolution (협력적 공진화 차등진화)

  • Shin, Seong-Yoon;Lee, Hyun-Chang;Shin, Kwang-Seong;Kim, Hyung-Jin;Lee, Jae-Wan
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.559-560
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    • 2021
  • Differential evolution is an efficient algorithm for solving continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems dramatically degrades performance and exponentially increases runtime. Therefore, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

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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.