• 제목/요약/키워드: Evolutionary Simulation

검색결과 189건 처리시간 0.022초

선체 외판 자동 생산 시스템의 시뮬레이션 기반 개발 (Simulation Based Design of an Automated Hull-piece Manufactruing System)

  • 손석제;신종계
    • 대한조선학회논문집
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    • 제36권4호
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    • pp.128-136
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    • 1999
  • 이 논문은 선체 외판 자동 생산 시스템의 효과적인 구축을 위하여 기존의 객체 지향 시스템 개발 이론을 개선한 ESBD(Evolutionary Simulation Based Design)을 제안한다. 목표 시스템인 AHMS(Automated Hull-piece Manufacturing System)는 가공 이전 평후판의 입고, 선처리, 절단, 1차 곡가공, 2차 곡가공 그리고 출고에 이르는 가상화된 자동화 공정의 분산 제어 시스템으로서 ESBD에 의해 개발된다. 실시간 원격 제품 모니터링(Product Monitoring)과 제품 정보 관리(Product Data Management)를 실현하기 위하여 분산 제어 및 물류 시뮬레이션을 통한 시스템 개발을 적용한다. 실험적으로 개발된 시스템은 새로운 공장의 배치 및 설계에 기본적인 가이드 라인을 제시하는 역할도 수행한다.

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해군분석모델용 AI-CGF를 위한 시나리오 생성 모델 설계(I): 진화학습 (Design of Scenario Creation Model for AI-CGF based on Naval Operations, Resources Analysis Model(I): Evolutionary Learning)

  • 김현근;강정석;박강문;김재우;김장현;박범준;지승도
    • 한국군사과학기술학회지
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    • 제25권6호
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    • pp.617-627
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    • 2022
  • Military training is an essential item for the fundamental problem of war. However, there has always been a problem that many resources are consumed, causing spatial and environmental pollution. The concepts of defense modeling and simulation and CGF(Computer Generated Force) using computer technology began to appear to improve this problem. The Naval Operations, Resources Analysis Model(NORAM) developed by the Republic of Korea Navy is also a DEVS(Discrete Event Simulation)-based naval virtual force analysis model. The current NORAM is a battle experiment conducted by an operator, and parameter values such as maneuver and armament operation for individual objects for each situation are evaluated. In spite of our research conducted evolutionary, supervised, reinforcement learning, in this paper, we introduce our design of a scenario creation model based on evolutionary learning using genetic algorithms. For verification, the NORAM is loaded with our model to analyze wartime engagements. Human-level tactical scenario creation capability is secured by automatically generating enemy tactical scenarios for human-designed Blue Army tactical scenarios.

A GENETIC ALGORITHM BY USE OF VIRUS EVOLUTIONARY THEORY FOR SCHEDULING PROBLEM

  • Saito, Susumu
    • 한국시뮬레이션학회:학술대회논문집
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    • 한국시뮬레이션학회 2001년도 The Seoul International Simulation Conference
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    • pp.365-370
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    • 2001
  • The genetic algorithm that simulates the virus evolutionary theory has been developed applying to combinatorial optimization problems. The algorithm in this study uses only one individual and a population of viruses. The individual is attacked, inflected and improved by the viruses. The viruses are composed of flour genes (a pair of top gene and a pair of tail gene). If the individual is improved by the attacking, the inflection occurs. After the infection, the tail genes are mutated. If the same virus attacks several times and fails to inflect, the top genes of the virus are mutated. By this mutation, the individual can be improved effectively. In addition, the influence of the immunologic mechanism on evolution is simulated.

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Game Theory Based Coevolutionary Algorithm: A New Computational Coevolutionary Approach

  • Sim, Kwee-Bo;Lee, Dong-Wook;Kim, Ji-Yoon
    • International Journal of Control, Automation, and Systems
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    • 제2권4호
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    • pp.463-474
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    • 2004
  • Game theory is a method of mathematical analysis developed to study the decision making process. In 1928, Von Neumann mathematically proved that every two-person, zero-sum game with many pure finite strategies for each player is deterministic. In the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) as introduced by Maynard Smith in 1982. Keeping pace with these game theoretical studies, the first computer simulation of coevolution was tried out by Hillis. Moreover, Kauffman proposed the NK model to analyze coevolutionary dynamics between different species. He showed how coevolutionary phenomenon reaches static states and that these states are either Nash equilibrium or ESS in game theory. Since studies concerning coevolutionary phenomenon were initiated, there have been numerous other researchers who have developed coevolutionary algorithms. In this paper we propose a new coevolutionary algorithm named Game theory based Coevolutionary Algorithm (GCEA) and we confirm that this algorithm can be a solution of evolutionary problems by searching the ESS. To evaluate this newly designed approach, we solve several test Multiobjective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by the coevolutionary algorithm and analyze the optimization performance of our algorithm by comparing the performance of our algorithm with that of other evolutionary optimization algorithms.

진화 알고리즘을 사용한 인간형 로봇의 동작 모방 학습 및 실시간 동작 생성 (Motion Imitation Learning and Real-time Movement Generation of Humanoid Using Evolutionary Algorithm)

  • 박가람;나성권;김창환;송재복
    • 제어로봇시스템학회논문지
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    • 제14권10호
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    • pp.1038-1046
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    • 2008
  • This paper presents a framework to generate human-like movements of a humanoid in real time using the movement primitive database of a human. The framework consists of two processes: 1) the offline motion imitation learning based on an Evolutionary Algorithm and 2) the online motion generation of a humanoid using the database updated bγ the motion imitation teaming. For the offline process, the initial database contains the kinetic characteristics of a human, since it is full of human's captured motions. The database then develops through the proposed framework of motion teaming based on an Evolutionary Algorithm, having the kinetic characteristics of a humanoid in aspect of minimal torque or joint jerk. The humanoid generates human-like movements far a given purpose in real time by linearly interpolating the primitive motions in the developed database. The movement of catching a ball was examined in simulation.

Multi-objective Optimization of a Laidback Fan Shaped Film-Cooling Hole Using Evolutionary Algorithm

  • Lee, Ki-Don;Husain, Afzal;Kim, Kwang-Yong
    • International Journal of Fluid Machinery and Systems
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    • 제3권2호
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    • pp.150-159
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    • 2010
  • Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

유전자 알고리즘을 이용한 타이어 공력소음의 저감 (Reduction of Air-pumping Noise based on a Genetic Algorithm)

  • 김의열;황성욱;김병현;이상권
    • 한국소음진동공학회논문집
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    • 제22권1호
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    • pp.61-73
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    • 2012
  • The paper presents the novel approach to solve some problems occurred in application of the genetic algorithm to the determination of the optimal tire pattern sequence in order to reduce the tire air-pumping noise which is generated by the repeated compression and expansion of the air cavity between tire pattern and road surface. The genetic algorithm has been used to find the optimal tire pattern sequence having a low level of tire air-pumping noise using the image based air-pumping model. In the genetic algorithm used in the previous researches, there are some problems in the encoding structure and the selection of objective function. The paper proposed single encoding element with five integers, divergent objective function based on evolutionary process and the optimal evolutionary rate based on Shannon entropy to solve the problems. The results of the proposed genetic algorithm with evolutionary process are compared with those of the randomized algorithm without evolutionary process on the two-dimensional normal distribution. It is confirmed that the genetic algorithm is more effective to reduce the peak value of the predicted tire air-pumping noise and the consistency and cohesion of the obtained simulation results are also improved in terms of probability.

Multi-objective Optimization Model with AHP Decision-making for Cloud Service Composition

  • Liu, Li;Zhang, Miao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권9호
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    • pp.3293-3311
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    • 2015
  • Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

시뮬레이션 기반 진화기법을 이용한 최적 보안 대응전략 자동생성 (Automated Generation of Optimal Security Defense Strategy using Simulation-based Evolutionary Techniques)

  • 이장세;황훈규;윤진식;박근우
    • 한국정보통신학회논문지
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    • 제14권11호
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    • pp.2514-2520
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    • 2010
  • 본 논문은 진화기법을 이용하여 최적의 보안 대응전략을 자동생성 하는 방법의 제안을 목적으로 한다. 정보통신 환경에 대한 침해에 의한 피해가 급증함에 따라 다양한 보안 기술에 대한 연구가 활발히 이루어지고 있다. 그러나 다양한 네트워크 환경에 대한 보안 기술들의 연통 상황을 고려한 최적의 대응 전략을 생성하는데 어려움이었다. 따라서 본 논문에서는 대응방법을 유전자로 표현하여 유전 알고리즘을 적용함으로써 대응방법들에 대한 최적의 조합으로서 최적 대응 전략을 생성하였다. 또한 시뮬레이션을 이용하여 다양한 상황에 대한 대용방법의 적용에 따른 취약성을 정량적으로 평가함으로써 적합도를 평가하였다. 끝으로 제안한 방법을 구현한 시스템에 대한 실험을 통하여 타당성을 검토하였다.

Simulation of nonstationary wind in one-spatial dimension with time-varying coherence by wavenumber-frequency spectrum and application to transmission line

  • Yang, Xiongjun;Lei, Ying;Liu, Lijun;Huang, Jinshan
    • Structural Engineering and Mechanics
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    • 제75권4호
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    • pp.425-434
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    • 2020
  • Practical non-synoptic fluctuating wind often exhibits nonstationary features and should be modeled as nonstationary random processes. Generally, the coherence function of the fluctuating wind field has time-varying characteristics. Some studies have shown that there is a big difference between the fluctuating wind field of the coherent function model with and without time variability. Therefore, it is of significance to simulate nonstationary fluctuating wind field with time-varying coherent function. However, current studies on the numerical simulation of nonstationary fluctuating wind field with time-varying coherence are very limited, and the proposed approaches are usually based on the traditional spectral representation method with low simulation efficiency. Especially, for the simulation of multi-variable wind field of large span structures such as transmission tower-line, not only the simulation is inefficient but also the matrix decomposition may have singularity problem. In this paper, it is proposed to conduct the numerical simulation of nonstationary fluctuating wind field in one-spatial dimension with time-varying coherence based on the wavenumber-frequency spectrum. The simulated multivariable nonstationary wind field with time-varying coherence is transformed into one-dimensional nonstationary random waves in the simulated spatial domain, and the simulation by wavenumber frequency spectrum is derived. So, the proposed simulation method can avoid the complicated Cholesky decomposition. Then, the proper orthogonal decomposition is employed to decompose the time-space dependent evolutionary power spectral density and the Fourier transform of time-varying coherent function, simultaneously, so that the two-dimensional Fast Fourier transform can be applied to further improve the simulation efficiency. Finally, the proposed method is applied to simulate the longitudinal nonstationary fluctuating wind velocity field along the transmission line to illustrate its performances.