• Title/Summary/Keyword: evolutionary optimal design

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Optimal Design of Nonlinear Structural Systems via EFM Based Approximations (진화퍼지 근사화모델에 의한 비선형 구조시스템의 최적설계)

  • 이종수;김승진
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2000.05a
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    • pp.122-125
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    • 2000
  • The paper describes the adaptation of evolutionary fuzzy model ins (EFM) in developing global function approximation tools for use in genetic algorithm based optimization of nonlinear structural systems. EFM is an optimization process to determine the fuzzy membership parameters for constructing global approximation model in a case where the training data are not sufficiently provided or uncertain information is included in design process. The paper presents the performance of EFM in terms of numbers of fuzzy rules and training data, and then explores the EFM based sizing of automotive component for passenger protection.

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Optimal seismic retrofit design method for asymmetric soft first-story structures

  • Dereje, Assefa Jonathan;Kim, Jinkoo
    • Structural Engineering and Mechanics
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    • v.81 no.6
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    • pp.677-689
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    • 2022
  • Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGA-II). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.

Co-Evolutionary Model for Solving the GA-Hard Problems (GA-Hard 문제를 풀기 위한 공진화 모델)

  • Lee Dong-Wook;Sim Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.3
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    • pp.375-381
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    • 2005
  • Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

Topology Design of Connection Component System Using Density Distribution Method (밀도분포법을 이용한 부재의 연결구조 최적화)

  • 한석영;유재원;박재용
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2003.04a
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    • pp.15-20
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    • 2003
  • Most engineering products contain more than one component. Failure occurs either at the connection itself or in the component at the point of attachment of the connection in many engineering structures. The allocation and design of connections such as bolts, spot-welds, adhesive etc. usually play an important role in the structure of multi-components. Topology optimization of connection component provides more practical solution in design of multi-component connection system. In this study, a topology optimization based on density distribution approach has been applied to optimal location of fasteners such as T-shape, L-shape and multi-component connection system. From the results, it was verified that the number of iteration was reduced, and the optimal topology was obtained very similarly comparing with ESO method. Therefore, it can be concluded that the density distribution method is very suitable for topology optimization of multi-component structures.

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A MMORPG Quest Reward Design Technique By Considering Optimal Quest Play Paths (최적 동선을 고려한 MMORPG 퀘스트 보상 설계 기법)

  • Kang, Shin-Jin;Shin, Seung-Ho;Cho, Sung-Hyun
    • Journal of Korea Game Society
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    • v.9 no.4
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    • pp.57-66
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    • 2009
  • A quest system is one of the important parts in the MMORPG (Massive Multiplayer Online Role Playing Game) contents. Because of its complexity in combining various content components, quest reward design belongs to a complicated work in estimating quest reward levels correctly in the initial development stage. In this paper, we suggest a new quest reward design technique by considering optimal quest play paths. We model a quest reward problem as the TSP (Traveling Salesman Problem) and solve that by adopting genetic algorithms. With our system, game designers easily estimate the optimal quest play path and it can be useful in reducing the trial-errors in the initial quest design process.

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Optimum Design of Integer and Fractional-Order PID Controllers for Boost Converter Using SPEA Look-up Tables

  • Amirahmadi, Ahmadreza;Rafiei, Mohammadreza;Tehrani, Kambiz;Griva, Giovanni;Batarseh, Issa
    • Journal of Power Electronics
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    • v.15 no.1
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    • pp.160-176
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    • 2015
  • This paper presents a method of designing optimal integer- and fractional-order proportional-integral-derivative (FOPID) controllers for a boost converter to gain a set of favorable characteristics at various operating points. A Pareto-based multi-objective optimization approach called strength Pareto evolutionary algorithm (SPEA) is used to obtain fast and low overshoot start-up and dynamic responses and switching stability. The optimization approach generates a set of optimal gains called Pareto set, which corresponds to a Pareto front. The Pareto front is a set of optimal results for objective functions. These results provide designers with a trade-off look-up table, in which they can easily choose any of the optimal gains based on design requirements. The SPEA also overcomes the difficulties of tuning the FOPID controller, which is an extension to the classic integer-order PID controllers and potentially promises better results. The proposed optimized FOPID controller provides an excellent start-up response and the desired dynamic response. This paper presents a detailed comparison of the optimum integer- and the fractional-order PID controllers. Extensive simulation and experimental results prove the superiority of the proposed design methodology to achieve a wide set of desired technical goals.

A study on the structure evolution of neural networks using genetic algorithms (유전자 알고리즘을 이용한 신경회로망의 구조 진화에 관한 연구)

  • 김대준;이상환;심귀보
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.223-226
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    • 1997
  • Usually, the Evolutionary Algorithms(EAs) are considered more efficient for optimal, system design because EAs can provide higher opportunity for obtaining the global optimal solution. This paper presents a mechanism of co-evolution consists of the two genetic algorithms(GAs). This mechanism includes host populations and parasite populations. These two populations are closely related to each other, and the parasite populations plays an important role of searching for useful schema in host populations. Host population represented by feedforward neural network and the result of co-evolution we will find the optimal structure of the neural network. We used the genetic algorithm that search the structure of the feedforward neural network, and evolution strategies which train the weight of neuron, and optimize the net structure. The validity and effectiveness of the proposed method is exemplified on the stabilization and position control of the inverted-pendulum system.

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Development of Global Function Approximations of Desgin optimization Using Evolutionary Fuzzy Modeling

  • Kim, Seungjin;Lee, Jongsoo
    • Journal of Mechanical Science and Technology
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    • v.14 no.11
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    • pp.1206-1215
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    • 2000
  • This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

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Control System Synthesis Using BMI: Control Synthesis Applications

  • Chung, Tae-Jin;Oh, Hak-Joon;Chung, Chan-Soo
    • International Journal of Control, Automation, and Systems
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    • v.1 no.2
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    • pp.184-193
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    • 2003
  • Biaffine Matrix Inequality (BMI) is known to provide the most general framework in control synthesis, but problems involving BMI's are very difficult to solve because nonconvex optimization should be solved. In the previous paper, we proposed a new solver for problems involving BMI's using Evolutionary Algorithms (EA). In this paper, we solve several control synthesis examples such as Reduced-order control, Simultaneous stabilization, Multi-objective control, $H_{\infty}$ optimal control, Maxed $H_2$ / $H_{\infty}$control design, and Robust $H_{\infty}$ control. Each of these problems is formulated as the standard BMI form, and solved by the proposed algorithm. The performance in each case is compared with those of conventional methods.

Optimisation of bridge deck positioning by the evolutionary procedure

  • Guan, Hong;Steven, G.P.;Querin, O.M.;Xie, Y.M.
    • Structural Engineering and Mechanics
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    • v.7 no.6
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    • pp.551-559
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    • 1999
  • This paper presents some simple thinking on an age-old question that given a bridge of a certain span and loading, from the point of view of the structural efficiency, where should the bridge deck be positioned? Generally, this decision is made for other reasons than structural efficiency such as aesthetics and the analyst is often presented with a fait accompli. Using the recently invented Evolutional Structural Optimisation (ESO) method, it is possible to demonstrate that having the deck at different vertical locations can lead to a very different mass and shape for each structural form resembling cable-stayed and cable-truss bridges. By monitoring a performance index which is the function of stresses and volume of discretised finite elements, the best optimised structure can be easily determined and the bridge deck positioning problem can be efficiently solved without resorting to any complex analysis procedures.