• Title/Summary/Keyword: Evolutionary Strategy

검색결과 199건 처리시간 0.022초

투영신경회로망의 훈련을 위한 진화학습기법 (Evolutionary Learning Algorithm fo r Projection Neural NEtworks)

  • 황민웅;최진영
    • 한국지능시스템학회논문지
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    • 제7권4호
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    • pp.74-81
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    • 1997
  • 본 논문에서는 시그모이드 함수와 방사형 기저 함수 모두를 생성시킬 수 있는 특별한 은닉층 노드를 갖는 투영신경회로망에 대하여 알아롭고 그것을 훈련시키기 위한 진화 학습 기법을 제시한다. 제시된 기법은 신경회로망의 매개변수와 연결 가충치뿐만 아니라, 어떤 목적함수를 나타내기 위한 최적의 은닉층 노드개수 또한 구조 최적화를 위한 진화연산자를 통해 찾아낸다. 각각의 은닉층 노드의 역할은 진화를 거듭하면서 방사형 기저 함수를 나타낼지 시그모이드 함수를 나타낼지 결정된다. 알고리즘을 구현하기 위해서 투영신경회로망은 연결 고리 리스트 자료구조로 나타내었다. 모의 실험에서 기존으 오차역전파에 의한 학습과 구조 성장 방식보다 적은 노드로 투영신경회로망을 훈련시킬 수 있음을 볼수 있다.

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바이러스-진화 유전 알고리즘을 이용한 퍼지 모델링 (Fuzzy Modeling Using Virus-Evolutionary Genetic Algorithm)

  • 이승준;주영훈;박진배
    • 한국지능시스템학회논문지
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    • 제10권5호
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    • pp.432-441
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    • 2000
  • 본 논문은 기존의 수학적인 모델링으로는 만족스러운 결과를 얻기 어려운 복잡하고 불확실한 비선형 시스템에 대한 퍼지 모델링 기법을 다룬다. 유전 알고리듬은 어느 정도 최적해를 전역적으로 찾을 수 있기 때문에 퍼지 모델링시에 파라미커와 구조를 동정하기 위하여 사용되었다. 하지만, 유전 알고리듬은 개체군이 유전적 다양성을 잃었을 경우 조기 수렴한다는 문제점이 있으며 바이러스-진화 유전 알고리듬은 이러한 지역수렴에 대한 방아닝 될 수 있다. 따라서, 본 논문에서는 바이러스 이론이 적용된 VEGA를 퍼지 모델링 할 때 이용할 수 있는 방법을 제안한다. 이 방법에서는 지역정보가 개체군 내에서 교환됨으로써 유전적 다양성을 유지하게 된다. 마지막으로, 본 논문에서 제안한 방법의 우수성과 일반성을 평가하기 위해 몇 가지의 수치적 예제를 제공한다.

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A Procedure for Robust Evolutionary Operations

  • Kim, Yongyun B.;Byun, Jai-Hyun;Lim, Sang-Gyu
    • International Journal of Quality Innovation
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    • 제1권1호
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    • pp.89-96
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    • 2000
  • Evolutionary operation (EVOP) is a continuous improvement system which explores a region of process operating conditions by deliberately creating some systematic changes to the process variable levels without jeopardizing the product. It is aimed at securing a satisfactory operating condition in full-scale manufacturing processes, which is generally different from that obtained in laboratory or pilot plant experiments. Information on how to improve the process is generated from a simple experimental design. Traditional EVOP procedures are established on the assumption that the variance of the response variable should be small and stable in the region of the process operation. However, it is often the case that process noises have an influence on the stability of the process. This process instability is due to many factors such as raw materials, ambient temperature, and equipment wear. Therefore, process variables should be optimized continuously not only to meet the target value but also to keep the variance of the response variables as low as possible. We propose a scheme to achieve robust process improvement. As a process performance measure, we adopted the mean square error (MSE) of the replicate response values on a specific operating condition, and used the Kruskal-Wallis test to identify significant differences between the process operating conditions.

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Multiobjective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement

  • Li, Zhihuan;Li, Yinhong;Duan, Xianzhong
    • Journal of Electrical Engineering and Technology
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    • 제5권1호
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    • pp.70-78
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    • 2010
  • Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

Optimized AI controller for reinforced concrete frame structures under earthquake excitation

  • Chen, Tim;Crosbie, Robert C.;Anandkumarb, Azita;Melville, Charles;Chan, Jcy
    • Advances in concrete construction
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    • 제11권1호
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    • pp.1-9
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    • 2021
  • This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

Many-objective joint optimization for dependency-aware task offloading and service caching in mobile edge computing

  • Xiangyu Shi;Zhixia Zhang;Zhihua Cui;Xingjuan Cai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권5호
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    • pp.1238-1259
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    • 2024
  • Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependency-aware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub-task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.

Modeling and Stimulating Node Cooperation in Wireless Ad Hoc Networks

  • Arghavani, Abbas;Arghavani, Mahdi;Sargazi, Abolfazl;Ahmadi, Mahmood
    • ETRI Journal
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    • 제37권1호
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    • pp.77-87
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    • 2015
  • In wireless networks, cooperation is necessary for many protocols, such as routing, clock synchronization, and security. It is known that cooperator nodes suffer greatly from problems such as increasing energy consumption. Therefore, rational nodes have no incentive to cooperatively forward traffic for others. A rational node is different from a malicious node. It is a node that makes the best decision in each state (cooperate or non-cooperate). In this paper, game theory is used to analyze the cooperation between nodes. An evolutionary game has been investigated using two nodes, and their strategies have been compared to find the best one. Subsequently, two approaches, one based on a genetic algorithm (GA) and the other on learning automata (LA), are presented to incite nodes for cooperating in a noisy environment. As you will see later, the GA strategy is able to disable the effect of noise by using a big enough chromosome; however, it cannot persuade nodes to cooperate in a noisefree environment. Unlike the GA strategy, the LA strategy shows good results in a noise-free environment because it has good agreement in cooperation-based strategies in both types of environment (noise-free and noisy).

A Study on the National Spatial Data Infrastructure of U.S.A

  • Koh, June-Hwan
    • 한국측량학회지
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    • 제25권6_1호
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    • pp.485-497
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    • 2007
  • By the rapid development of Information Communication Technology (ICT) and Geo-spatial Technology (GT) and the increased usage of spatial data for planning and infrastructure management, the National Geographic Information System (NGIS) for more efficient and effective utilization of spatial information has been developed by the central government in Korea since 1995. NGIS is the base of Spatial Data Infrastructure (SDI). SDI is developed as one of National Information Infrastructures (NII). Among the hierarchy of SDI, National Spatial Data Infrastructure (NSDI) has very important role in the success of SDI development. Many research articles show that the USA's NSDI initiatives, development strategy have been strongly influenced all over the world. In these viewpoints, to propose the future directions of Korean NGIS, the development of NSDI strategy of USA is reviewed by literature through published book and internet resources. The conclusions of this study are as follow: 1) top-down and bottom-up approach are needed for integrated data sharing and standardization. 2) the creative and evolutionary vision and strategy has to be suggested. 3) the training program and lecture material has to be developed and diffused to the users and providers of spatial data. 4) governance system has to be built for NSDI evaluation. 5) the formation of geo-spatial forum to discuss the spatial-related problems and make research agenda, etc.

진화계산 기반 인공에이전트를 이용한 교섭게임 (Bargaining Game using Artificial agent based on Evolution Computation)

  • 성명호;이상용
    • 디지털융복합연구
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    • 제14권8호
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    • pp.293-303
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    • 2016
  • 근래에 진화 연산을 활용한 교섭 게임의 분석은 게임 이론 분야에서 중요한 문제로 다루어지고 있다. 본 논문은 교섭 게임에서 진화 연산을 사용하여 이기종 인공 에이전트 간의 상호 작용 및 공진화 과정을 조사하였다. 교섭게임에 참여하는 진화전략 에이전트들로서 유전자 알고리즘(GA), 입자군집최적화(PSO) 및 차분진화알고리즘(DE) 3종류를 사용하였다. GA-agent, PSO-agent 및 DE-agent의 3가지 인공 에이전트들 간의 공진화 실험을 통해 교섭게임에서 가장 성능이 우수한 진화 계산 에이전트가 무엇인지 관찰 실험하였다. 시뮬레이션 실험결과, PSO-agent가 가장 성능이 우수하고 그 다음이 GA-agent이며 DE-agent가 가장 성능이 좋지 않다는 것을 확인하였다. PSO-agent가 교섭 게임에서 성능이 가장 우수한 이유를 이해하기 위해서 게임 완료 후 인공 에이전트 전략들을 관찰하였다. PSO-agent는 거래 실패로 인해 보수를 얻지 못하는 것을 감수하고서라도 가급적 많은 보수를 얻기 위한 방향으로 진화하였다는 것을 확인하였으며, 반면에 GA-agent와 DE-agent는 소량의 보수를 얻더라도 거래를 성공시키는 방향으로 진화하였다는 것을 확인하였다.

우리나라 광대역망 구축의 정책기조 (B-ISDN evolution strategy from Korean perspective)

  • 김범환;서승우
    • 기술경영경제학회:학술대회논문집
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    • 기술경영경제학회 1997년도 제11회 하계학술발표회 논문집
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    • pp.286-306
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    • 1997
  • 본고는 정보통신망의 경제적관점을 중점적으로 하여 향후 광대역정보통신망구축이 경제사회에 미치는 영향을 국가경쟁력과 삶의 질 측면에서 분석하였다. 즉 광대역망이 갖는 생산재, 소비재적인 특성과 기술개발과 투자시점간의 시차에 따른 경제적 효과를 고려하였다. 또한 이러한 경제적 접근이외에도 광대역망구축에 대한 현실적인 접근을 위하여 진화단계 특성 등 기술적인 측면을 고려하여 실현가능한 시나리오를 제시하고 그에 따른 영향을 추가하였다. 이에 따라 기술개발 선점에 따른 망구축을 실현하는 기술기반 우위국가는 국가 전반적인 경쟁력을 강화시킬 수 있을 뿐만 아니라 국민의 삶의 질 향상에도 기여할 것임이 제시된다. 또한 우리나라와 같이 기술수준이 낮은 기술기반 열위국가의 경우에 실현가능한 3가지 시나리오를 고려하였다.

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