• Title/Summary/Keyword: evolution algorithm

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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.

Structural damage detection using a multi-stage improved differential evolution algorithm (Numerical and experimental)

  • Seyedpoor, Seyed Mohammad;Norouzi, Eshagh;Ghasemi, Sara
    • Smart Structures and Systems
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    • v.21 no.2
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    • pp.235-248
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    • 2018
  • An efficient method utilizing the multi-stage improved differential evolution algorithm (MSIDEA) as an optimization solver is presented here to detect the multiple-damage of structural systems. Natural frequency changes of a structure are considered as a criterion for damage occurrence. The structural damage detection problem is first transmuted into a standard optimization problem dealing with continuous variables, and then the MSIDEA is utilized to solve the optimization problem for finding the site and severity of structural damage. In order to assess the performance of the proposed method for damage identification, an experimental study and two numerical examples with considering measurement noise are considered. All the results demonstrate the effectiveness of the proposed method for accurately determining the site and severity of multiple-damage. Also, the performance of the MSIDEA for damage detection compared to the standard differential evolution algorithm (DEA) is confirmed by test examples.

Design and Implementation of a Stochastic Evolution Algorithm for Placement (Placement 확률 진화 알고리즘의 설계와 구현)

  • 송호정;송기용
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.1
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    • pp.87-92
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    • 2002
  • Placement is an important step in the physical design of VLSI circuits. It is the problem of placing a set of circuit modules on a chip to optimize the circuit performance. The most popular algorithms for placement include the cluster growth, simulated annealing and integer linear programming. In this paper we propose a stochastic evolution algorithm searching solution space for the placement problem, and then compare it with simulated annealing by analyzing the results of each implementation.

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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.

Parameter Estimation of Shallow Arch Using Quantum-Inspired Evolution Algorithm (양자진화 알고리즘을 이용한 얕은 아치의 파라미터 추정)

  • Shon, Sudeok;Ha, Junhong
    • Journal of Korean Association for Spatial Structures
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    • v.20 no.1
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    • pp.95-102
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    • 2020
  • The structural design of arch roofs or bridges requires the analysis of their unstable behaviors depending on certain parameters defined in the arch shape. Their maintenance should estimate the parameters from observed data. However, since the critical parameters exist in the equilibrium paths of the arch, and a small change in such the parameters causes a significant change in their behaviors. Thus, estimation to find the critical ones should be carried out using a global search algorithm. In this paper we study the parameter estimation for a shallow arch by a quantum-inspired evolution algorithm. A cost functional to estimate the system parameters included in the arch consists of the difference between the observed signal and the estimated signal of the arch system. The design variables are shape, external load and damping constant in the arch system. We provide theoretical and numerical examples for estimation of the parameters from both contaminated data and pure data.

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.

Optimal Allocation Method of Hybrid Active Power Filters in Active Distribution Networks Based on Differential Evolution Algorithm

  • Chen, Yougen;Chen, Weiwei;Yang, Renli;Li, Zhiyong
    • Journal of Power Electronics
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    • v.19 no.5
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    • pp.1289-1302
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    • 2019
  • In this paper, an optimal allocation method of a hybrid active power filter in an active distribution network is designed based on the differential evolution algorithm to resolve the harmonic generation problem when a distributed generation system is connected to the grid. A distributed generation system model in the calculation of power flow is established. An improved back/forward sweep algorithm and a decoupling algorithm are proposed for fundamental power flow and harmonic power flow. On this basis, a multi-objective optimization allocation model of the location and capacity of a hybrid filter in an active distribution network is built, and an optimal allocation scheme of the hybrid active power filter based on the differential evolution algorithm is proposed. To verify the effect of the harmonic suppression of the designed scheme, simulation analysis in an IEEE-33 nodes model and an experimental analysis on a test platform of a microgrid are adopted.

Optimal Control System of Traverse Grinding (트래버스 연삭의 최적 제어시스템)

  • Choi, Jeongju
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.13 no.12
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    • pp.5704-5708
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    • 2012
  • In this paper, the algorithm to determine the optimal condition of traverse grinding is proposed by using differential evolution algorithm(DEA). The cost function to determine the optimal grinding condition is designed with considering process cost, production rate, surface roughness. Also, the constraint conditions for grinding such as thermal damage effect, machine tool stiffness, wear parameter of grinding wheel, surface roughness are considered. The algorithm is implemented with LabView software which is widely used at the industrial field. The performance of proposed algorithm is verified by comparing with the result of genetic algorithm(GA) through computer simulation.

Development of Optimum Design Program of Sinusoidal Corrugated Web Beam Using Differential Evolution Algorithm (차분진화알고리즘을 이용한 정현파형 주름웨브보의 최적설계프로그램 개발)

  • Shon, Su-Deok;Yoo, Mi-Na;Lee, Seung-Jae;Kang, Joo-Won
    • Journal of the Architectural Institute of Korea Structure & Construction
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    • v.29 no.12
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    • pp.21-28
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    • 2013
  • Corrugated steel plates offer several advantages that make them preferred materials for use in various structures including bridges and steel frames. Aside from aircraft design, the corrugated steel plate has also been applied to container, bridges, PEB system, etc. Corrugated steel plates have also been the subject of various research efforts. However, these studies focus on the strength of the beams and the buckling analysis of corrugated steel plates. Optimum design of corrugated web beam remains insufficient. Therefore, this study focuses on the structural design program of sinusoidal corrugated web steel beams using Differential Evolution algorithm. A simple beam model is selected as a numerical example to verify the program. The optimum program is compared with the program using Genetic Algorithm.