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Solving Pattern Nesting Problems with genetic algorithms Employing Task Decomposition and Contact Detection

EVOLUTIONARY COMPUTATION 1995年第3期3卷 239-266页

作者： Dighe, Rahul Jakiela, Mark J. MIT Dept Mech Engn Comp Aided Design Lab Cambridge MA 02139 USA

A hierarchical approach for nesting two-dimensional shapes based on genetic algorithms is described. For the higher-level search, a representation that facilitates genetic search based on recombination is developed. An alternative to overlap computation based on assembly of polygons is used at the lower level-of search. Two implementations to find minimum-area enclosures for polygons, with and without a constraint on the width of stock, are discussed. Sample output illustrating the effectiveness of the approach is provided.

关键词： Shape packing pattern nesting hierarchical genetic algorithm industrial application

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Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

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International Journal of Hybrid Intelligent Systems 2021年第1-2期17卷 15-32页

作者： Melin, Patricia Sánchez, Daniela Tijuana Institute of Technology Tijuana Mexico

Diabetes has become a global health problem, where a proper diagnosis is vital for the life quality of patients. In this article, a genetic algorithm is put forward for designing type-2 fuzzy inference systems to perform Diabetes Classification. We aim at finding parameter values of Type-2 Trapezoidal membership functions and the type of model (Mamdani or Sugeno) with this optimization. To verify the effectiveness of the proposed approach, the PIMA Indian Diabetes dataset is used, and results are compared with type-1 fuzzy systems. Five attributes are used considered as the inputs of the fuzzy inference systems to obtain a Diabetes diagnosis. The instances are divided into design and testing sets, where the design set allows the genetic algorithm to minimize the error of classification, and finally, the real behavior of the fuzzy inference system is validated with the testing set. © 2021 - IOS Press. All rights reserved.

关键词： diabetes classification hierarchical genetic algorithm optimization PIMA Indian diabetes type-1 fuzzy logic type-2 fuzzy logic

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Implementation of advanced genetic algorithm to modern power system stabilization control

Implementation of advanced genetic algorithm to modern power...

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IEEE PES Power Systems Conference and Exposition

作者： Hongesombut, K Mitani, Y Kyushu Inst Technol Dept Elect Engn Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)078038718X

This paper focuses on the use of advanced techniques in genetic algorithm for solving power system stabilization control problems. At the outset, the proposed hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel micro-GA) are proposed and then they win be extended to solve two example problems. In the first example, these techniques are applied to simultaneously tune power system stabilizers (PSSs). The PSSs are optimally tuned to simultaneously shift the lightly damped and undamped oscillation modes to a stable zone in the s-plane and to self identify the appropriate PSS locations. In second example, parallel micro-GA is used to design the fuzzy logic controller (FLC) of STATCOM. The applied technique for designing a FLC helps us save time and does not require experts for designing. From these results, we can see that these advanced techniques provide enhanced versatility for solving problems in power system stabilization control.

关键词： power system stabilization advanced genetic algorithm hierarchical genetic algorithm parallel micro genetic algorithm

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USE hierarchical genetic PARTICLE FILTER TO FIGURE ARTICULATED HUMAN TRACKING

USE HIERARCHICAL GENETIC PARTICLE FILTER TO FIGURE ARTICULAT...

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IEEE International Conference on Multimedia and Expo (ICME 2008)

作者： Ye, Long Zhang, Qin Guan, Ling Commun Univ China Informat Engn Sch Beijing Peoples R China Ryerson Univ Ryerson Multimedia Res Lab Toronto ON Canada

ISBN: (纸本)9781424425709

Using particle filter to track human movement, a key problem is how to draw samples in high-dimensional state space. In this paper, we present a novel framework of particle filtering, namely hierarchical genetic Particle Filter (HGPF), to improve the efficiency of samples by a hierarchical evolutionary detection. As a result, we can obtain reasonably distributed samples thus translating into reliable tracking performance. Finally, we apply the technique to 2D articulated human movement tracking. Result demonstrates the effectiveness of HGPF in solving the tracking problem like self-occlusion and cluttered background.

关键词： Particle Filtering hierarchical genetic algorithm Articulated Human Movement Tracking Self-Occlusion High-dimensional Optimization

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Multi-objective optimization for modular granular neural networks applied to pattern recognition

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INFORMATION SCIENCES 2018年 460卷 594-610页

作者： Melin, Patricia Sanchez, Daniela Tijuana Inst Technol Tijuana Mexico

A new method for Modular Neural Network optimization based on a Multi-objective hierarchical genetic algorithm is proposed in this paper. The modular neural network using a granular approach and its optimization using a multi-objective hierarchical genetic algorithm provides better results than when the modular neural network is applied without a granular approach and optimization of parameters. The optimization of different parameters of the modular granular neural network architecture, such as the number of modules (sub-granules), size of the dataset for the training phase, goal error, learning algorithm, number of hidden layers and their respective number of neurons are performed in the proposed method. The fitness functions aim at minimizing the size of the dataset for the training phase and the error using a multi-objective approach. This method can be used in different areas of application, such as human recognition, classification problems or time series prediction. In this case the proposed method is tested with human recognition based on the face and ear biometric measures, where the proposed method aims at finding non dominated solutions based on the number of data points for training and the recognition error. Benchmark face and ear databases are used to illustrate the advantages of the proposed approach. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Modular neural networks Granular computing hierarchical genetic algorithm Human recognition Multi-objective optimization Pattern recognition Face recognition Ear recognition

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Management planning of fast-growing plantations based on a bi-level programming model

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FOREST POLICY AND ECONOMICS 2014年 38卷 173-177页

作者： Zhai, Wenyuan Zhao, Yingzi Lian, Xiangru Yang, Mingming Lu, Fadian Shandong Agr Univ Coll Forestry Tai An 271018 Shandong Peoples R China

The management of fast-growing plantations involves two-layer decision makers, such as forestry management departments and direct operators. Direct operators are interested in the economic benefits of plantation management, while forestry management departments aim to preserve reasonable age structures and sustainable development within plantations. A hi-level programming model should be suitable for optimizing the management of fast-growing plantations. Based on the current management situation in China, which is characterized by decentralized management on a small scale and a lack of integrated planning, we established a nonlinear bi-level programming model to improve the management of fast-growing plantations through the comprehensive consideration of upper- and lower-level targets. An effective hierarchical genetic algorithm was applied to the model, and the specific steps in the solution process were reported. Finally, the scientific value of the model and the availability of the algorithm are confirmed using an example of a fast-growing plantation. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Fast-growing plantation Management planning Bi-level programming model hierarchical genetic algorithm

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Adaptive fuzzy identification and predictive control for industrial processes

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第17期40卷 6964-6975页

作者： Mendes, Jerome Araujo, Rui Souza, Francisco Univ Coimbra Dept Elect & Comp Engn DEEC UC Inst Syst & Robot ISR UC PT-3030290 Coimbra Portugal

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T-S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T-S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm's performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T-S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system;and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T-S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Fuzzy identification hierarchical genetic algorithm Predictive fuzzy control Fuzzy generalized predictive control

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Greenfield distribution network expansion strategy with hierarchical GA and MCDEA under uncertainty

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 79卷 245-252页

作者： Verma, Mandhir Kumar Mukherjee, V. Yadav, Vinod Kumar Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India Galgotias Univ Dept Elect Engn Gb Nagar Uttar Pradesh India

Distribution network expansion planning (DNEP) is becoming more complex in nature. Addition of new load centers, due to increasing conversion of greenfield areas into habitats, have generated need of more intense and highly structured planning strategies. Micro level work on distribution expansion planning has been ignored by most of the researchers mainly in Indian scenario. Since practical distribution networks are quite large, number of candidates (load centers) will be more and, hence, number of variables (electrical parameters and new load center feasible connections with the existing system) are remarkable. Optimizing a large system may result in significant decrease of accuracy and increase of computation time. For deciphering this issue, segmentation procedure has been applicable. For this purpose, a sensitivity analysis has been applied to find dependent variables. It is obvious that a correct segmentation can decrease computation time (as a single task is operated in segments simultaneously) while accuracy decreases negligibly. In present work, a scheme has been introduced to connect three greenfield load centers with existing primary distribution system by using hierarchical genetic algorithm (HGA). HGA is an integrated approach of analytical hierarchical process and genetic algorithm. The paper reports best selection of investment with finest voltage profile and least losses while maintaining radiality of the system. DNEP has been done at micro level and proposed methodology has been tested on a small dimension practical distribution system. The novelty of this paper is to optimize the best possible selection of connection of new load centers with existing system with the help of AHP and GA and results have been verified with advance optimal tool multiple criteria data envelopment analysis (MCDEA). HGA and MCDEA are applied to practical nine bus distribution system and the results are presented and compared. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distribution network expansion planning Analytical hierarchy process hierarchical genetic algorithm Load flow analysis Multiple criteria data envelopment analysis Decision making units

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Wireless communication network design in IC factory

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2001年第2期48卷 452-459页

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

A wireless local area network (WLAN) is designed for an IC factory in Hong Kong using the hierarchical genetic algorithm (HGA), The HG,S is capable of handling multiobjective functions and discrete constraints. Because of this uniqueness, together with the adopting of a Pareto ranking scheme, a solution can be reached even when skewed multiobjective functions and constraints confinements are being imposed. It has been found from this study that a precise number of base stations can be identified for the WLAN network, while it can satisfy a number of objectives es and constraints, This added feature provides a further design tradeoff between cost and performance at no extra effort.

关键词： hierarchical genetic algorithm multiobjective wireless LAN

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Automatic sensor placement for model-based robot vision

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第1期34卷 393-408页

作者： Chen, SY Li, YF City Univ Hong Kong Dept Mfg Engn & Engn Management Kowloon Hong Kong Peoples R China

This paper presents a method for automatic sensor placement for model-based robot vision. In such a vision system, the sensor often needs to be moved from one pose to another around the object to observe all features of interest. This allows multiple three-dimensional (3-D) images to be taken from different vantage viewpoints. The task involves determination of the optimal sensor placements and a shortest path through these viewpoints. During the sensor planning, object. features are resampled as individual points attached with surface normals. The optimal sensor placement graph is achieved by a genetic algorithm in which a min-max criterion is used for the evaluation. A shortest path is determined by Christofides algorithm. A Viewpoint Planner is developed to generate the sensor placement plan. It includes many functions, such as 3-D animation of the object geometry, sensor specification, initialization of the viewpoint number and their distribution, viewpoint evolution, shortest path computation, scene simulation of a specific viewpoint, parameter amendment. Experiments are also carried out on a real robot vision system to demonstrate the effectiveness of the proposed method.

关键词： christofides algorithm hierarchical genetic algorithm robot vision sensor placement viewpoints

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