• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.408-410
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• 1994

This Paper Presents a learning strategy for neural networks based on genetic algorithms and evolution strategies. Genetic algorithms and evolution strategies are used to train weights of feedforward neural network to solve problems faster than neural network, especially backpropagation. Simulations are performed exclusive-OR problem, full-adder problem, sine function generator to demonstrate the effectiveness of neural-GA-ES.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.77-81
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• 2003

SDM (Structural Dynamics Modification) is a tool to improve dynamic characteristics of a structure, more specifically of a base structure, by adding or deleting auxiliary (modifying) structures. In this paper, the goal of the optimal SDM is set to maximize the natural frequency of a base plate structure by attaching serially-connected beam stiffeners. The design variables are chosen as positions of the attaching beam stiffeners, where the number of stiffeners is considered as a design space. The problem of non-matching interface nodes between the base plate and beam stiffeners is solved by using localized Lagrange multipliers, which act to glue the two structures with non-matching interface nodes. As fer the cases of non-matching interface nodes problem, the governing equation of motion of a structure can be considered from the viewpoint of a topological modification, which involves the change of the number of structural members and DOFs. Consequently, the eigenpairs of the beam-stiffened plate structure are obtained by using an eigen reanalysis technique of topological modifications. Evolution Strategies (ES), which is a probabilistic population-based optimization technique that mimics the principles from biological evolution in nature, is utilized as a mean for the optimization.

• Advances in Computational Design
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• 제1권4호
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• 대한조선학회 특별논문집
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• 대한조선학회 2005년도 특별논문집
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• pp.63-68
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• 2005

Optimum design of the big brackets is performed through iterated 3-D FE analyses to meet the permissible limits of stress, which consumes an excessive amount of calculation time. Therefore, this study has been prepared to determine rapidly and accurately an optimum size and scantling of the big brackets at the initial design stage. The generalized slope deflection method (GSDM) based on the span point concept is applied to enhance the efficiency of iterated structural analyses. The accuracy and applicability of the present method is verified by comparing with a detail 3-D FE analysis of web frame structures. As an optimization technique, evolution strategies (ES) are applied using discrete design variables for practical design.

• 대한조선학회논문집
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• 제40권6호
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• pp.12-19
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• 2003

Corrugated bulkheads for a bulk carrier are divided into watertight bulkheads and deep tank bulkheads. Design of the watertight bulkheads is principally determined by the permissible limit of Classification and IACS requirements. But, the verification of strength through finite element analysis is indispensable for design of the deep tank bulkheads. A stage for stress evaluation of corrugated part is required for optimum structural design of the deep tank bulkheads. Since the finite element analysis for real model requires excessive amount of calculation time, in this study one corrugated structure is replaced with beam element and is idealized as 2 dimensional frame structure connected to upper and lower stool Minimum weight design of the deep tank bulkheads is performed through generalized sloped deflection method(GSDM) as direct calculation method. The purpose of this study is the development of design system for the minimization of steel weight of deep tank bulkheads as well as watertight bulkheads. Discrete variables are used as design variables for the practical design. Evolution strategies(ES) is used as an optimization technique.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.33.1-33
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• 2001

We address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identification here is to provide the optimal mathematical model of both the linear dynamic and the nonlinear static parts in some appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while we estimate the linear dynamic system part by the least squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1269-1271
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• 1996

This paper presents a visual servoing combined by evolutionary algorithms and neural network for a robotic manipulators to control position and orientation of the end-effector. Using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we generate the control input to agree the target image, to realize the visual servoing. The validity and effectiveness of the proposed control scheme will be verified by computer simulations.

• 전기학회논문지
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• 제56권10호
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.

• 한국지능시스템학회논문지
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• 제12권1호
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• pp.61-66
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• 2002

본 논문에서는 Flexible Manipulator의 제어를 위해 퍼지제어의 제약인 멤버쉽 함수, 퍼지규clr을 유전알고리즘으로 조정, 최적화 하는 새로운 제어기를 설계하였다. 사용된 유전알고리즘은 Steady State Genetic 알고리즘과 Adaptive 유전 알고리즘의 합성이다. 제안한 제어기는 Flexible Manipulator의 끝점 무게 0.8kmg, 최대속도 1m/s의 경우, 퍼지제어에 비해 오차가 90.8% 감소하고 신경회로망을 이용한 퍼지제어에 비하여는 31.8% 감소하였으며 진화전략과 퍼지제어합성에 의한 제어기보다는 오차가 31.3% 감소하는 통 제어성능과 그 유용성이 우수함을 확인하였다.

• 한국지능시스템학회논문지
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• 제14권3호
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• pp.253-261
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• 2004

게임 이론은 의사 결정 문제와 관련 된 연구와 함께 정립 된 수학적 분석법으로써 1928년 Von Neumann이 유한개의 순수전략이 존재하는 2인 영합게임은 결정적(deterministic)이라는 것을 증명함으로써 수학적 기반을 정립하였고 50년대 초, Nash는 Von Neumann의 이론을 일반화하는 개념을 제안함으로써 현대적 게임이론의 장을 열었다. 이후 진화 생물학 연구자들에 의해 고전적인 게임 이론의 가정에 해당하는 참가자들의 합리성(rationality) 대신 다윈 선택(Darwinian selection)에 의해 게임의 해를 탐색하는 것이 가능하다는 것이 밝혀지게 되었고 진화 생물학자 Maynard Smith에 의해 진화적 안정 전략(Evolutionary Stable Strategy: ESS)의 개념이 정립되면서 현대적 게임 이론으로써 진화적 게임 이론이 체계화 되었다. 한편 이와 같은 진화적 게임 이론에 관한 연구와 함께 생태계의 공진화를 이용한 컴퓨터 시뮬레이션이 1991년 Hillis에 의해 처음으로 시도되었으며 Kauffman은 다른 종들 간의 공진화적 동역학(dynamics)을 분석하기 위한 NK 모델을 제안하였다. Kauffman은 이 모델을 이용하여 공진화 현상이 어떻게 정적 상태(static state)에 이르며 이 상태들은 게임 이론에서 소개되어진 내쉬 균형이나 ESS에 해당한다는 것을 보여주었다. 이후, 몇몇 연구자들 게임 이론과 진화 알고리즘에 기반한 연산 모델들을 제시해 왔으나 실용적인 문제의 적용에 대한 연구는 아직 미흡한 편이다. 이에 본 논문에서는 게임 이론에 기반 한 공진화 알고리즘을(Game theory based Co-Evolutionary Algorithm: GCEA) 제안하고 이 알고리즘을 이용하여 공진화적인 문제들을 효과적으로 해결할 수 있음을 확인하는 것을 목표로 한다.