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hierarchical genetic algorithms for topology optimization in fuzzy control systems

INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2007年第5期36卷 575-591页

作者： Castillo, Oscar Valdez, Fevrier Melin, Patricia Tijuana Inst Technol Div Grad Studies & Res Chula Vista CA 91909 USA

We describe in this paper the use of hierarchical genetic algorithms (HGA) for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The HGA enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with two cases of intelligent control. Simulation results for both applications show that we are able to find an optimal set of rules and membership functions for the fuzzy control system.

关键词： fuzzy control system hierarchical genetic algorithms membership functions neural network

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hierarchical genetic algorithms for Fuzzy Inference System Optimization Applied to Response Integration for Pattern Recognition 13

Hierarchical Genetic Algorithms for Fuzzy Inference System O...

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13th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Sanchez, Daniela Melin, Patricia Castillo, Oscar Tijuana Inst Technol Tijuana 22379 BC Mexico

ISBN: (纸本)9783319136509;9783319136493

In this paper, a new method for fuzzy inference system optimization is proposed. The optimization consists in find the optimal parameters of fuzzy inference system used to combine the responses of modular neural networks using a hierarchical genetic algorithm. The optimized parameters are: type of fuzzy logic (type-1 and interval type-2), type of system (Mamdani or Sugeno), type of membership functions, number of membership functions in each variable (inputs and output), their parameters and the consequents of the fuzzy rules. Four benchmark databases are used to test the proposed method where, each database is a different biometric measure (face, iris, ear and voice) and each database is learned by a modular neural network. The main objective of the fuzzy inference system is to combine the different responses of the modular neural network and achieve final good results even when one (o more) biometric measure has individually a bad result. The results obtained in a previous work are used to compare with the results obtained in this paper.

关键词： Modular Neural Networks Type-1 Fuzzy Logic Interval Type-2 Fuzzy Logic hierarchical genetic algorithms Pattern Recognition Human Recognition Biometric Measures

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Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2009年第4期94卷 891-904页

作者： Kumar, Ranjan Izui, Kazuhiro Yoshimura, Masataka Nishiwaki, Shinji Kyoto Univ Yoshimura Lab Dept Aeronaut & Astronaut Sakyo Ku Kyoto 6068501 Japan

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems, The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Multilevel redundancy allocation Reliability optimization Multi-objective optimization hierarchical genetic algorithms

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Active flow control of airfoil using mesh/meshless methods coupled to hierarchical genetic algorithms for drag reduction design

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ENGINEERING COMPUTATIONS 2013年第4期30卷 562-580页

作者： Wang, Hong Leskinen, Jyri Lee, Dong-Seop Periaux, Jacques Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland Int Ctr Numer Methods Engn Barcelona Spain

Purpose - The purpose of this paper is to investigate an active flow control technique called Shock Control Bump (SCB) for drag reduction using evolutionary algorithms. Design/methodology/approach - A hierarchical genetic algorithm (HGA) consisting of multi-fidelity models in three hierarchical topological layers is explored to speed up the design optimization process. The top layer consists of a single sub-population operating on a precise model. On the middle layer, two sub-populations operate on a model of intermediate accuracy. The bottom layer, consisting of four sub-populations (two for each middle layer populations), operates on a coarse model. It is well-known that genetic algorithms (GAs) are different from deterministic optimization tools in mimicking biological evolution based on Darwinian principle. In HGAs process, each population is handled by GA and the best genetic information obtained in the second or third layer migrates to the first or second layer for refinement Findings - The method was validated on a real life optimization problem consisting of two-dimensional SCB design optimization installed on a natural laminar flow airfoil (RAE5243). Numerical results show that HGA is more efficient and achieves more drag reduction compared to a single population based GA. Originality/value - Although the idea of HGA approach is not new, the novelty of this paper is to combine it with mesh/meshless methods and multi-fidelity flow analyzers. To take the full benefit of using hierarchical topology, the following conditions are implemented: the first layer uses a precise meshless Euler solver with fine cloud of points, the second layer uses a hybrid mesh/meshless Euler solver with intermediate mesh/clouds of points, the third layer uses a less fine mesh with Euler solver to explore efficiently the search space with large mutation span.

关键词： hierarchical genetic algorithms Mesh/meshless method Inverse problems Shock Control Bump Drag reduction Flow Aerodynamics

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Fuzzy supervisory PI controllers for a binary distillation column using hierarchical genetic algorithms

Fuzzy supervisory PI controllers for a binary distillation c...

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IEEE Region 10 Conference on Analog and Digital Techniques in Electrical Engineering

作者： Worapradya, K Pratishthananda, S Chulalongkorn Univ Fac Engn Dept Elect Engn Bangkok 10330 Thailand

ISBN: (纸本)0780385608

This paper presents the optimum fuzzy supervisory PI controllers of a binary distillation column. A hierarchical genetic Algorithm (HGA) is used to define the optimal number and shape of membership functions, and fuzzy rules. The optimum fuzzy system then adapts the parameters, K-P and K-I, of the PI controller. Two optimum fuzzy supervisory PI controllers are developed to maintain the top and bottom product composition due to changes in feed flow rate. Simulation results are compared with the PI controllers using minimum ISTSE method.

关键词： optimal control fuzzy systems fuzzy control PI control genetic algorithms MIMO systems hierarchical systems optimum fuzzy supervisory PI controllers binary distillation column hierarchical genetic algorithms fuzzy rules feed flow rate ISTSE method

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Probabilistic Roadmaps and hierarchical genetic algorithms for Optimal Motion Planning

Probabilistic Roadmaps and Hierarchical Genetic Algorithms f...

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Science and Information Conference (SAI)

作者： Lakhdari, Abdelhalim Achour, Nouara USTHB Univ LRPE Lab Algiers Algeria

ISBN: (纸本)9780989319317

In this paper we present a motion planning algorithm that combines between Probabilistic Roadmaps (PRM) and hierarchical genetic algorithms (HGA) in order to generate optimal motions for a non holonomic mobile robot. PRM are used to generate a set of paths that will be optimized by HGA, the obtained trajectory leads a non holonomic mobile robot from an initial to a final configuration while maintaining feasibility and no-collision with obstacles.

关键词： Path planning PRM hierarchical genetic algorithms robotics

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Motion Planning for Car-like Robots Using hierarchical genetic algorithms

Motion Planning for Car-like Robots Using Hierarchical Genet...

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World Congress on Engineering (WCE 2013)

作者： Achour, N. Lakhdari, A. Ferguene, F. USTHB Univ Sci & Technol Lab LRPE Algiers Algeria

ISBN: (纸本)9789881925282

The problem of optimal motion generation is still a major focus of research in mobile robotics. Although many solutions have been proposed, it has always had to take into account new factors and constraints that may be related either to the environment (obstacles) or the robot itself. For a car-like vehicle, nonholonomic kinematic constraints force the robot to follow a trajectory imposed by the angle of its steering wheels. In this paper, we present an algorithm for motion planning in order to generate optimal movements for a nonholonomic mobile robot using a probabilistic network method (PRNI) associated with two artificial intelligence techniques: the A (star) algorithm to search for the shortest path and genetic algorithms for optimization. We used the hierarchical genetic algorithms (AGH) that played on two criteria: collision and path length to calculate the optimal path without collision. One of the key points of our approach lies in the evaluation of the individuals;we used the principle of artificial potential field that has yielded interesting results in terms of quality and optimization of trajectories.

关键词： path Motion planning PRM hierarchical genetic algorithms robotics

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Optimization of Fuzzy Logic Controller Based Maximum Power Point Tracking Using hierarchical genetic algorithms 9

Optimization of Fuzzy Logic Controller Based Maximum Power P...

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9th International Conference on Smart Grid (icSmartGrid)

作者： Guenounou, Ouahib Belkaid, Abdelhakim Colak, Ilhami Dahhou, Boutaib Chabour, Ferhat Univ Bejaia Lab Technol Ind & Informat Bejaia Algeria Nisantasi Univ Dept Elect Elect Engn Istanbul Turkey CNRS LAAS 7 Ave Colonel Roche F-31077 Toulouse 4 France Univ UPS Toulouse France Univ Le Havre GREAH Lab Le Havre France

ISBN: (纸本)9781665445320;9781665445313

Fuzzy logic controller (FLC)-based maximum power point tracking (MPPT) or simply fuzzy MPPT technique has attracted a large interest because it allows a more human approach to control and shows good performance compared to conventional techniques. However, in spite of the many advantages of the fuzzy MPPT, the design of this controller remains a difficult task due to the large number of parameters and fuzzy rules that needs an optimal tuning. This paper describes a new design method of fuzzy MPPT for photovoltaic conversion system using hierarchical genetic algorithms (HGAs). The chromosomes of HGAs are organized in hierarchical structure and able to optimize all parameters of fuzzy MPPT like the number of fuzzy rules and its consequents, and the membership functions parameters. The optimized fuzzy MPPT is tested in simulation and showed optimal responses even under rapid variations in irradiance. The proposed approach is compared to other fuzzy MPPT design methods currently in use, and the results confirm the superiority of our approach.

关键词： Photovoltaic system hierarchical genetic algorithms rule base size control genes parameter genes

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Optimal Control of a Nano-Positioning Stage Using Linear Matrix Inequality and hierarchical genetic algorithms

Optimal Control of a Nano-Positioning Stage Using Linear Mat...

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IEEE International Conference on System, Man, and Cybernetic

作者： Yu, Gwo-Ruey Haung, Lun-Wei Natl Ilan Univ Dept Elect Engn Ilan City Taiwan

ISBN: (纸本)9781424423835

This paper presents optimal fuzzy control of a nano-positioning system by Linear Matrix Inequality (LMI) and hierarchical genetic algorithms (HGAs). First, the Bouc-Wen model describes the nonlinear hysteresis curve of a piezoelectric actuator. Then, the Takagi-Sugeno (T-S) fuzzy model approximates the Bouc-Wen equation. The parallel distributed compensation (PDC) is designed to control the nonlinear nano-positioning system. The HGAs are applied to search the optimal membership functions, state feedback gains, and rulebase of T-S fuzzy model. The stability of the nonlinear nano-positioning system is guaranteed based on the positive-definite solution of LMI.

关键词： nano-positioning system linear matrix inequality hierarchical genetic algorithms

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Total system cost minimization in CDMA PCS network using hierarchical genetic algorithms

Total system cost minimization in CDMA PCS network using hie...

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27th Annual Conference of the IEEE Industrial-Electronics-Society

作者： Chan, TM Kwong, S Man, KF Tang, KS City Univ Hong Kong Kowloon Hong Kong Peoples R China

ISBN: (纸本)0780371089

This paper deals with the minimization of the total system cost in the PCS network using CDMA cellular system standard. The total system cost includes the installation costs of Base Stations (BSs), Base Station Controllers (BSCs) and PCS Exchanges (PCXs), the weighted-distance connection costs between BSs and BSCs and between BSCs and PCXs and the hard handoff cost. Our objective is to keep each of these costs as a minimum with guaranteed service performance. However, there exists tradeoff property between these costs. Therefore, we use an efficient and effective searching technique, hierarchical genetic algorithms (HGA), which has the ability to handle multiobjective functions and various constraints and find multiple different solutions for decision making.

关键词： Code Division Multiple Access (CDMA) Personal Communication Services hierarchical genetic algorithms

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