A Self-learning Optimization Technique for Topology Design of Computer Networks

2008 ◽  
pp. 38-51
Author(s):  
Angan Das ◽  
Ranga Vemuri
Keyword(s):  
Computer Networks ◽  
Optimization Technique ◽  
Topology Design ◽  
Self Learning

Genetic Algorithm-Based Structural Topology Design With Compliance and Topology Simplification Considerations

1996 ◽  
Vol 118 (1) ◽  
pp. 89-98 ◽  
Author(s):  
C. D. Chapman ◽  
M. J. Jakiela
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Optimization Technique ◽  
Topology Design ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Design Representation ◽  
Homogenization Methods ◽  
Future Work ◽  
Structural Topology Design

The genetic algorithm (GA), an optimization technique based on the theory of natural selection, is applied to structural topology design problems. After reviewing the genetic algorithm and previous research in structural topology optimization, we detail the chromosome-to-design representation which enables the genetic algorithm to perform structural topology optimization. Extending our prior investigations, this article first compares our genetic-algorithm-based technique with homogenization methods in the minimization of a structure’s compliance subject to a maximum volume constraint. We then use our technique to generate topologies combining high structural performance with a variety of material connectivity characteristics which arise directly from our discretized design representation. After discussing our findings, we describe potential future work.

Topology Optimization of Flywheel Rotors Using SIMP Method: A Preliminary Study

2012 ◽  
Vol 579 ◽  
pp. 427-434 ◽  
Author(s):  
T.D. Tsai ◽  
C.C. Cheng
Keyword(s):  
Topology Optimization ◽  
Energy Storage ◽  
High Speed ◽  
Optimization Technique ◽  
Design Sensitivity ◽  
Topology Design ◽  
Energy Storage Devices ◽  
Simp Method ◽  
Preliminary Study ◽  
Rotor Stiffness

Flywheels are kinetic energy storage and retrieval devices as chemical batteries. However, the high charge and discharge rates, as well as the high cycling capability make flywheels attractive as compared to other energy storage devices. This research serves as a preliminary study that aims for developing a technique in designing a flywheel rotor based on the solid isotropic method with penalization (SIMP) method. Examples are presented to illustrate the optimum structural layouts obtained given various design objectives. For a static rotor, the objectives are maximizing the first torsional natural frequency, maximizing the moment of inertia and maximizing both of them, respectively. The problem is formulated using bound formulation and the method of moving asymptotes (MMA), a first-order optimization technique, was employed. Therefore the design sensitivity becomes a necessity. The so-called checkerboard problem in the topology optimization is avoided using the nodal design variable. Also, a threshold is used to reduce the numerical imperfection in each iteration. For the topology design of a rotating rotor, the centrifugal force induced in the high-speed rotation is considered. The objective is to maximize the rotor stiffness and is demonstrated in the last example. Results show clear topology layout of flywheel was obtained using proposed method.

scholarly journals Genetic Algorithms As an Approach to Configuration and Topology Design

1993 ◽  
Author(s):  
Colin D. Chapman ◽  
Kazuhiro Saitou ◽  
Mark J. Jakiela
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Structural Optimization ◽  
Optimization Technique ◽  
Topology Design ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Element Analysis ◽  
Structure Topology ◽  
Design Representation

Abstract The Genetic Algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology optimization. Given a structure’s boundary conditions and maximum allowable design domain, a discretized design representation is created. Populations of genetic algorithm “chromosomes” are then mapped into the design representation, creating potentially optimal structure topologies. Utilizing genetics-based operators such as crossover and mutation, generations of increasingly-desirable structure topologies are created. In this paper, the use of the genetic algorithm (GA) in structural topology optimization is presented. An overview of the genetic algorithm will describe the genetics-based representations and operators used in a typical genetic algorithm search. After defining topology optimization and its relation to the broader area of structural optimization, a review of previous research in GA-based and non-GA-based structural optimization is provided. The design representations, and methods for mapping genetic algorithm “chromosomes” into structure topology representations, are then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of beam cross-section topologies and cantilevered plate topologies, and we also investigate efficient techniques for using finite element analysis in a genetic algorithm-based search. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.

scholarly journals Bio-Inspired Structure and Behavior of Self-Recovery Quadruped Robot with a Limited Number of Functional Legs

2019 ◽  
Vol 9 (4) ◽  
pp. 799 ◽  
Author(s):  
Sarun Chattunyakit ◽  
Yukinori Kobayashi ◽  
Takanori Emaru ◽  
Ankit Ravankar
Keyword(s):  
Structural Design ◽  
Numerical Optimization ◽  
Control Method ◽  
Optimization Technique ◽  
Quadruped Robot ◽  
Robotic Platform ◽  
Recovery Method ◽  
Q Learning ◽  
And Behavior ◽  
Self Learning

In this study, the authors focus on the structural design of and recovery methods for a damaged quadruped robot with a limited number of functional legs. Because the pre-designed controller cannot be executed when the robot is damaged, a control strategy to avoid task failures in such a scenario should be developed. Not only the control method but also the shape and structure of the robot itself are significant for the robot to be able to move again after damage. We present a caterpillar-inspired quadruped robot (CIQR) and a self-learning mudskipper inspired crawling (SLMIC) algorithm in this research. The CIQR is realized by imitating the prolegs of caterpillars and by using a numerical optimization technique. A reinforcement learning method called Q-learning is employed to improve the adaptability of locomotion based on the crawling behavior of mudskipper. The results show that the proposed robotic platform and recovery method can improve the moving ability of the damaged quadruped robot with a few active legs in both simulations and experiments. Moreover, we obtained satisfactory results showing that a damaged multi-legged robot with at least one leg could travel properly along the required direction. Furthermore, the presented algorithm can successfully be employed in a damaged quadruped robot with fewer than four legs.

3D-Printable Unit Cell Design for Cubic and Orthotropic Porous Microstructures Using Topology Optimization Based on Optimality Criteria Algorithm

2018 ◽  
Vol 10 (06) ◽  
pp. 1850060 ◽  
Author(s):  
Alireza Moshki ◽  
Akbar Ghazavizadeh ◽  
Ali Asghar Atai ◽  
Mostafa Baghani ◽  
Majid Baniassadi
Keyword(s):  
Topology Optimization ◽  
Optimization Technique ◽  
Optimality Criteria ◽  
Unit Cell ◽  
Topology Design ◽  
Two Phase ◽  
Topology Identification ◽  
Young’S Moduli ◽  
Periodic Microstructures ◽  
Porous Microstructures

Optimal design of porous and periodic microstructures through topology identification of the associated periodic unit cell (PUC) constitutes the topic of this work. Here, the attention is confined to two-phase heterogeneous materials in which the topology identification of manufacturable 3D-PUC is conducted by means of a topology optimization technique. The associated objective function is coupled with 3D numerical homogenization approach that connects the elastic properties of the 3D-PUC to the target product. The topology optimization methodology that is adopted in this study is the combination of solid isotropic material with penalization (SIMP) method and optimality criteria algorithm (OCA), referred to as SIMP-OCA methodology. The fairly simple SIMP-OCA is then generalized to handle the topology design of 3D manufacturable microstructures of cubic and orthotropic symmetry. The performance of the presented methodology is experimentally validated by fabricating real prototypes of extremal elastic constants using additive manufacturing. Experimental evaluation is performed on two designed microstructures: an orthotropic sample with Young’s moduli ratios [Formula: see text], [Formula: see text] and a cubic sample with negative Poisson’s ratio of [Formula: see text]. In all practical examples studied, laboratory measurements are in reasonable agreement with the prescribed values; thus, corroborating the applicability of the proposed methodology.

Genetic Algorithm-Based Structural Topology Design With Compliance and Manufacturability Considerations

1994 ◽  
Author(s):  
Colin D. Chapman ◽  
Mark J. Jakiela
Keyword(s):  
Genetic Algorithm ◽  
Optimization Technique ◽  
Weight Ratio ◽  
Topology Design ◽  
Structural Topology ◽  
Programming Technique ◽  
Design Representation ◽  
Homogenization Methods ◽  
Future Work ◽  
Structural Topology Design

Abstract The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to structural topology design problems with compliance and manufacturability considerations. After describing the genetic algorithm and reviewing previous research in structural topology design, we detail the chromosome-to-design representation which enables the genetic algorithm to perform structural topology optimization. Extending our prior investigations, this article details the use of our genetic algorithm-based technique to minimize a structure’s compliance, subject to a maximum volume constraint. The resulting structure is then directly compared with a solution obtained using a mathematical programming technique and material homogenization methods. We also demonstrate how our technique can generate structures which combine high stiffness-to-weight ratio with high manufacturability. After a brief discussion of our findings, we describe potential future work in genetic algorithm-based structural topology design.

scholarly journals Genetic Algorithms as an Approach to Configuration and Topology Design

1994 ◽  
Vol 116 (4) ◽  
pp. 1005-1012 ◽  
Author(s):  
C. D. Chapman ◽  
K. Saitou ◽  
M. J. Jakiela
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Optimization Technique ◽  
Topology Design ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Design Representation ◽  
Future Work ◽  
Structural Topology Design ◽  
Algorithm Search

The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design. An overview of the genetic algorithm will first describe the genetics-based representations and operators used in a typical genetic algorithm search. Then, a review of previous research in structural optimization is provided. A discretized design representation, and methods for mapping genetic algorithm “chromosomes” into this representation, is then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of cantilevered plate topologies, and we investigate methods for optimizing finely-discretized design domains. The genetic algorithm’s ability to find families of highly-fit designs is also examined. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.

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