Novel Form-Finding of Tensegrity Structures Using Ant Colony Systems

2012 ◽  
Vol 4 (3) ◽  
Author(s):  
Yao Chen ◽  
Jian Feng ◽  
Yongfen Wu
Keyword(s):  
Ant Colony ◽  
Physical Models ◽  
Ant Colony System ◽  
Form Finding ◽  
Tensegrity Structures ◽  
Stress States ◽  
Feasible Solutions ◽  
The Stability ◽  
Ant Colony Systems ◽  
External Loads

Tensegrity structures have remarkable configurations and are drawing the attention of architects and engineers. They possess inextensional mechanisms and self-stress states at a static equilibrium configuration under no external loads. For geometry with its nodes fixed, different connectivity patterns of the compression bars and tension cables might bring some novel tensegrity structures. Thus, form-finding is the key to designing novel tensegrity structures. Here, we develop a discrete optimization model for the form-finding and convert it into a modified traveling salesman problem (TSP). The ant colony system (ACS) is used to search for feasible solutions, where all the predetermined nodes are taken as different cities in the network. An objective function that considers the stability and the relative stiffness is developed to obtain the optimized configurations of tensegrity structures. Examples based on some regular geometries (including a hexagon and two polyhedra) and two nonregular geometries are carried out using the proposed technique. Many different configurations of the pin-jointed assemblies are transformed into interesting tensegrity structures. To verify the proposed method, some physical models are constructed and compared to the tensegrity structures obtained from the form-finding process. We conclude that this novel algorithm can be applicable to the form-finding of both regular and nonregular tensegrity structures.

Novel Form-Finding of Tensegrity Structures Using Ant Colony Systems

2011 ◽  
Author(s):  
Yao Chen ◽  
Jian Feng ◽  
Yongfen Wu
Keyword(s):  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Ant Colony System ◽  
Form Finding ◽  
Tensegrity Structures ◽  
Connectivity Patterns ◽  
Feasible Solutions ◽  
Ant Colony Systems ◽  
The Given

Tensegrity structures are drawing the attention of architects and engineers due to their remarkable configurations. They have inextensional mechanisms, yet they are stable. The determination of connectivity patterns of the compression bars and tension cables is a key to design tensegrity structures. In this paper, a discrete optimization model for the form-finding of tensegrity structures was developed, and converted into a modified travelling salesman problem (TSP). The ant colony system (ACS) was used to search for feasible solutions, where all the given nodes were taken as different cities in the network. To obtain optimized shapes of tensegrity structures with stable equilibriums and adequate stiffness, an objective function was introduced. Examples based on the geometries of some polyhedra were carried out using the proposed technique. Many different configurations of the assemblies which consist of cables and bars are transformed into interesting tensegrity structures. It concludes that this novel algorithm could be applicable to the form-finding of both regular and nonregular tensegrity structures.

scholarly journals A novel method for determining the feasible integral self-stress states for tensegrity structures

2021 ◽  
Vol 8 (1) ◽  
pp. 70-88
Author(s):  
Aguinaldo Fraddosio ◽  
Gaetano Pavone ◽  
Mario Daniele Piccioni
Keyword(s):  
Form Finding ◽  
Tensegrity Structures ◽  
Innovative Method ◽  
Symmetry Properties ◽  
Nodal Coordinates ◽  
Novel Method ◽  
Equilibrium Matrix ◽  
Stress States ◽  
Value Decomposition ◽  
External Loads

Abstract The form-finding analysis is a crucial step for determining the stable self-equilibrated states for tensegrity structures, in the absence of external loads. This form-finding problem leads to the evaluation of both the self-stress in the elements and the shape of the tensegrity structure. This paper presents a novel method for determining feasible integral self-stress states for tensegrity structures, that is self-equilibrated states consistent with the unilateral behaviour of the elements, struts in compression and cables in tension, and with the symmetry properties of the structure. In particular, once defined the connectivity between the elements and the nodal coordinates, the feasible self-stress states are determined by suitably investigating the Distributed Static Indeterminacy (DSI). The proposed method allows for obtaining feasible integral self-stress solutions by a unique Singular Value Decomposition (SVD) of the equilibrium matrix, whereas other approaches in the literature require two SVD. Moreover, the proposed approach allows for effectively determining the Force Denstiy matrix, whose properties are strictly related to the super-stability of the tensegrity structures. Three tensegrity structures were studied in order to assess and discuss the efficiency and accuracy of the proposed innovative method.

scholarly journals STUDY AND APPLY ACO ALGORITHM IN TIME-COST OPTIMIZATION OF CONSTRUCTION PROJECT

2010 ◽  
Vol 13 (1) ◽  
pp. 17-30
Author(s):  
Luan Hong Pham ◽  
Nhan Thanh Duong
Keyword(s):  
Cost Optimization ◽  
Construction Project ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Time Cost ◽  
Project Duration ◽  
Proposed Model ◽  
Feasible Solutions ◽  
Computational Resources

Time-cost optimization problem is one of the most important aspects of construction project management. In order to maximize the return, construction planners would strive to optimize the project duration and cost concurrently. Over the years, many researches have been conducted to model the time-cost relationships; the modeling techniques range from the heuristic method and mathematical approach to genetic algorithm. In this paper, an evolutionary-based optimization algorithm known as ant colony optimization (ACO) is applied to solve the multi-objective time-cost problem. By incorporating with the modified adaptive weight approach (MAWA), the proposed model will find out the most feasible solutions. The concept of the ACO-TCO model is developed by a computer program in the Visual Basic platforms. An example was analyzed to illustrate the capabilities of the proposed model and to compare against GA-based TCO model. The results indicate that ant colony system approach is able to generate better solutions without making the most of computational resources which can provide a useful means to support construction planners and managers in efficiently making better time-cost decisions.

Post-Disassembly Part-Machine Clustering Using Artificial Neural Networks and Ant Colony Systems

2013 ◽  
pp. 135-150 ◽  
Keyword(s):  
Complex Problem ◽  
Ant Colony ◽  
System Structure ◽  
Ant Colony System ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Reusable Components ◽  
Clustering Problem ◽  
Ant Colony Systems

After transhipment, the remanufacturable parts/components are usually released to the reprocessing facility where the necessary operations (such as disassembly) are performed. At times, formation of parts/components for reprocessing operations is a complex problem with broad implications to an organization, both on system structure and system operations. The chapter starts with an introduction about the issue of the classification of disassembled and reusable components. Then the related studies dealing with similar problems in the literature are discussed in the background section. Next, the focal problem of this chapter is stated in the problem statement section. The authors formulate the problem as a part-machine clustering problem in which, according to similarities of reprocessing requirement, disassembled parts/components are grouped into families, and machines are organized as cells. A detailed description about the approach (i.e., adaptive resonance theory neural network and ant colony system) can be found in the proposed methodology section. Right after this, two illustrative examples are explained in the experimental study section. The potential research directions regarding the main problem considered in this chapter are highlighted in the future trends section. Finally, the conclusion drawn in the last section closes this chapter.

Study on the Sky Luminance Distribution of Information Methods by Ant Colony Systems

2011 ◽  
Vol 48-49 ◽  
pp. 1202-1207 ◽  
Author(s):  
Ying He ◽  
Ping Guo ◽  
Yong Lin
Keyword(s):  
Lighting Design ◽  
Ant Colony ◽  
Distribution Model ◽  
Ant Colony System ◽  
Standard Work ◽  
Luminance Distribution ◽  
Easy Calculation ◽  
Complex Optimization ◽  
Ant Colony Systems ◽  
Calculation Software

The sky luminance distribution model studied by information methods use a new arithmetic to solve complex optimization heuristic algorithm, ant colony system. Through analyzing and optimizing the effect factors of sky luminance by the ant colony system, the study got the coefficients value of sky relative luminance distribution numerical expressions. Based on the study, develop a set of convenient and practical sky luminance model calculation software, which can provide an intuitive and easy calculation. This model has higher precision. It provides valuable results for day lighting using, especially high precise and standard work of day lighting design both in theory and application.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110192
Author(s):  
Songcan Zhang ◽  
Jiexin Pu ◽  
Yanna Si ◽  
Lifan Sun
Keyword(s):  
Path Planning ◽  
Convergence Rate ◽  
Mobile Robot ◽  
Local Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Geometric Features ◽  
Robot Path Planning ◽  
Computation Efficiency ◽  
Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

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