• Title/Summary/Keyword: Intelligent optimization methods

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Intelligent fuzzy inference system approach for modeling of debonding strength in FRP retrofitted masonry elements

  • Khatibinia, Mohsen;Mohammadizadeh, Mohammad Reza
    • Structural Engineering and Mechanics
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    • v.61 no.2
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    • pp.283-293
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    • 2017
  • The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

Immune Algorithm Controller Design of DC Motor with parameters variation (DC 모터 파라메터 변동에 대한 면역 알고리즘 제어기 설계)

  • 박진현;전향식;이민중;김현식;최영규
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2002.05a
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    • pp.175-178
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    • 2002
  • The proposed immune algorithm has an uncomplicated structure and memory-cell mechanism as the optimization algorithm which imitates the principle of humoral immune response, and has been used as methods to solve parameter optimization problems. Up to now, the applications of immune algorithm have been optimization problems with non-varying system parameters. Therefore, the effect of memory-cell mechanism, which is a merit of immune algorithm, is without. this paper proposes the immune algorithm using a memory-cell mechanism which can be the application of system with nonlinear varying parameters. To verified performance of the proposed immune algorithm, the speed control of nonlinear DC motor are performed. Computer simulation studies show that the proposed immune algorithm has a fast convergence speed and a good control performances under the varying system parameters.

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Design of BAM using an Optimization approach (최적화기법을 이용한 BAM의 설계)

  • 권철희
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.2
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    • pp.161-167
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    • 2000
  • In this paper, we propose a design method for BAMs(bidirectiona1 associative memories) which can perform the function of bidirectional association efficiently. Based on the theoretical investigation about the properties of BAMs, we first formulate the problem of finding a BAM that can store the given pattern pairs as stable states with high error correction ratio in the form of a constrained optimization problem. Next, we transform the constrained optimization problem into a GEVP(genera1ized eigenvalue problem), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

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Design of GBSB Neural Network Using Solution Space Parameterization and Optimization Approach

  • Cho, Hy-uk;Im, Young-hee;Park, Joo-young;Moon, Jong-sup;Park, Dai-hee
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.1 no.1
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    • pp.35-43
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    • 2001
  • In this paper, we propose a design method for GBSB (generalized brain-state-in-a-box) based associative memories. Based on the theoretical investigation about the properties of GBSB, we parameterize the solution space utilizing the limited number of parameters sufficient to represent the solution space and appropriate to be searched. Next we formulate the problem of finding a GBSB that can store the given pattern as stable states in the form of constrained optimization problems. Finally, we transform the constrained optimization problem into a SDP(semidefinite program), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

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An Enhanced Genetic Algorithm for Optimization of Multimodal (다봉성 함수의 최적화를 위한 향상된 유전알고리듬의 제안)

  • 김영찬;양보석
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.5
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    • pp.373-378
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    • 2001
  • The optimization method based on an enhanced genetic algorithms is for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is a global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by single point method in reconstructive search space. Four numerical examples are also presented in this papers to comparing with conventional methods.

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Integrating Ant Colony Clustering Method to a Multi-Robot System Using Mobile Agents

  • Kambayashi, Yasushi;Ugajin, Masataka;Sato, Osamu;Tsujimura, Yasuhiro;Yamachi, Hidemi;Takimoto, Munehiro;Yamamoto, Hisashi
    • Industrial Engineering and Management Systems
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    • v.8 no.3
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    • pp.181-193
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    • 2009
  • This paper presents a framework for controlling mobile multiple robots connected by communication networks. This framework provides novel methods to control coordinated systems using mobile agents. The combination of the mobile agent and mobile multiple robots opens a new horizon of efficient use of mobile robot resources. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption in aggregation. The imaginary application is making "carts," such as found in large airports, intelligent. Travelers pick up carts at designated points but leave them arbitrary places. It is a considerable task to re-collect them. It is, therefore, desirable that intelligent carts (intelligent robots) draw themselves together automatically. Simple implementation may be making each cart has a designated assembly point, and when they are free, automatically return to those points. It is easy to implement, but some carts have to travel very long way back to their own assembly point, even though it is located close to some other assembly points. It consumes too much unnecessary energy so that the carts have to have expensive batteries. In order to ameliorate the situation, we employ mobile software agents to locate robots scattered in a field, e.g. an airport, and make them autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO). ACO is the swarm intelligence-based methods, and a multi-agent system that exploit artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. In this paper, we focus on the implementation of the controlling mechanism of the multi-robots using the mobile agents.

Intelligent Clustering in Vehicular ad hoc Networks

  • Aadil, Farhan;Khan, Salabat;Bajwa, Khalid Bashir;Khan, Muhammad Fahad;Ali, Asad
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.8
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    • pp.3512-3528
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    • 2016
  • A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

Learning of Adaptive Behavior of artificial Ant Using Classifier System (분류자 시스템을 이용한 인공개미의 적응행동의 학습)

  • 정치선;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.10a
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    • pp.361-367
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    • 1998
  • The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

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Desing of Genetic Algorithms Based Optimal Fuzzy Controller and Stabilization Control of the Inverted Pendulum System (유전알고리즘에 의한 최적 퍼지 제어기의 설계와 도립전자 시스템의 안정화 제어)

  • 박정훈;김태우;임영도;소명옥;이준탁
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1996.10a
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    • pp.162-165
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    • 1996
  • In this paper, we proposed an optimization method of the membership function and the numbers of fuzzy rule base for the stabilization controller of the inverted pendulum system by genetic algorithm(GAs). Conventional methods to these problems need to an expert knowledge or human experience. The proposed genetic algorithm method will tune automatically the input-output membership parameters and will optimize their rule-base.

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Optimizaton of A Fuzzy Adaptive Network for Control Applications

  • Esogbue, Augustine O.;Murrell, Janes A.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.1346-1349
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    • 1993
  • In this paper, we describe the use of certain optimization techniques, principally dynamic programming and high level computational methods, to enhance the capabilities of a fuzzy adaptive neural network controller which we had developed for on-line control and adaption on complex nonlinear processes. Potential applications to an array of processes from diverse fields are discussed.

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