A Simulated Annealing-Based Approach to Three-Dimensional Component Packing

1995 ◽  
Vol 117 (2A) ◽  
pp. 308-314 ◽  
Author(s):  
S. Szykman ◽  
J. Cagan
Keyword(s):  
Simulated Annealing ◽  
Mechanical Engineering ◽  
Three Dimensional ◽  
Layout Optimization ◽  
Vlsi Circuits ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Engineering Applications ◽  
Component Layout ◽  
General Component

This paper introduces a simulated annealing-based approach to three-dimensional component packing that employs simulated annealing to generate optimal solutions. Simulated annealing has been used extensively for two-dimensional layout of VLSI circuits; this research extends techniques developed for two-dimensional layout optimization to three-dimensional problems which are more representative of mechanical engineering applications. This research also provides a framework in which to solve general component layout problems.

A Simulated Annealing-Based Approach to Three Dimensional Component Packing

1994 ◽  
Author(s):  
Simon Szykman ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Three Dimensional ◽  
Packing Problem ◽  
Computational Approach ◽  
Vlsi Circuits ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Layout Problem ◽  
Component Layout ◽  
General Component

Abstract This paper introduces a computational approach to three dimensional component layout that employs simulated annealing to generate optimal solutions. Simulated annealing has been used extensively for two dimensional layout of VLSI circuits; this research extends techniques developed for two dimensional layout optimization to three dimensional problems which are more representative of mechanical engineering applications. In many of these applications, miniaturization trends increase the need to achieve higher packing density and fit components into smaller containers. This research addresses the three dimensional packing problem, which is a subset of the general component layout problem, as a framework in which to solve general layout problems.

Constrained Three-Dimensional Component Layout Using Simulated Annealing

1997 ◽  
Vol 119 (1) ◽  
pp. 28-35 ◽  
Author(s):  
S. Szykman ◽  
J. Cagan
Keyword(s):  
Simulated Annealing ◽  
Packing Density ◽  
Computational Algorithm ◽  
Three Dimensional ◽  
Spatial Constraints ◽  
Layout Algorithm ◽  
Component Layout ◽  
Density Fitting ◽  
Power Drill ◽  
General Component

This research introduces a computational algorithm that uses simulated annealing to optimize three-dimensional component layouts. General component layout problems are characterized by three objectives: achieving high packing density, fitting components into a given container and satisfying spatial constraints on components. This paper focuses on the extension of a simulated annealing packing algorithm to a general layout algorithm through the implementation of a language of spatial constraints that are characteristic of layout problems. These constraints allow the designer to specify desired component proximities or to restrict translation or rotation of components based on a global origin or set of coordinate axes, or relative to other component locations or orientations. The layout of components from a cordless power drill illustrates the algorithm.

Synthesis of Optimal Non-Orthogonal Routes

1995 ◽  
Author(s):  
Simon Szykman ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Three Dimensional ◽  
Routing Algorithms ◽  
Engineering Applications ◽  
Routing Optimization ◽  
Novel Approach

Abstract This paper introduces a novel approach to three dimensional routing optimization. Examples of routing tasks for engineering applications include routing of pipes, wires and air ducts. Traditionally, routing algorithms perform Manhattan, or orthogonal, routing. Non-orthogonal routing can be less costly than Manhattan routing and for applications such as automotive or aerospace design, Manhattan routing is impractical due to spatial limitations. The research presented in this paper uses simulated annealing as the basis of a non-orthogonal routing optimization algorithm that avoids the drawbacks associated with Manhattan routing. Several examples comparing the two approaches are given.

Synthesis of Optimal Nonorthogonal Routes

1996 ◽  
Vol 118 (3) ◽  
pp. 419-424 ◽  
Author(s):  
S. Szykman ◽  
J. Cagan
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Three Dimensional ◽  
Routing Algorithms ◽  
Engineering Applications ◽  
Routing Optimization ◽  
Novel Approach

This paper introduces a novel approach to three dimensional routing optimization. Examples of routing tasks for engineering applications include routing of pipes, wires and air ducts. Traditionally, routing algorithms perform Manhattan, or orthogonal, routing. Nonorthogonal routing can be less costly than Manhattan routing and for applications such as automotive or aerospace design, Manhattan routing is impractical due to spatial limitations. The research presented in this paper uses simulated annealing as the basis of a nonorthogonal routing optimization algorithm. Several examples comparing the two approaches are given.

Optimal Three-Dimensional Placement of Heat Generating Electronic Components

1997 ◽  
Vol 119 (2) ◽  
pp. 106-113 ◽  
Author(s):  
M. I. Campbell ◽  
C. H. Amon ◽  
J. Cagan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Three Dimensional ◽  
Heat Transfer Analysis ◽  
Placement Test ◽  
Test Cases ◽  
Electronic Components ◽  
Component Layout ◽  
Temperature Ranges ◽  
Annealing Algorithm

This work introduces an algorithm that uses simulated annealing to perform electronic component layout while incorporating constraints related to thermal performance. A hierarchical heat transfer analysis is developed which is used in conjunction with the simulated annealing algorithm to produce final layout configurations that are densely packed and operate within specified temperature ranges. Examples of three-dimensional component placement test cases are presented including an application to embedded wearable computers.

A Pattern Search-Based Algorithm for Three-Dimensional Component Layout

1998 ◽  
Author(s):  
Su Yin ◽  
Jonathan Cagan
Keyword(s):  
Three Dimensional ◽  
Pattern Search ◽  
Test Problems ◽  
Optimal Solutions ◽  
Local Minima ◽  
Objective Functions ◽  
Spatial Constraint ◽  
Routing Problem ◽  
Component Layout ◽  
Different Types

Abstract A pattern search-based algorithm is introduced for efficient component layout optimization. The algorithm is applicable to general layout problems, where component geometry can be arbitrary, design goals can be multiple and spatial constraint satisfactions can be of different types. Extensions to pattern search are introduced to help the algorithm to converge to optimal solutions by escaping inferior local minima. The performance on all of the test problems shows that the algorithm runs one-to-two orders of magnitude faster than a robust simulated annealing-based algorithm for results with the same quality. The algorithm is further extended to solve a concurrent layout and routing problem, which demonstrates the ability of the algorithm to apply new pattern strategies in search and to include different objective functions in optimization.

High Resolution Microscopy of Multi-Layered Biological Crystals

1982 ◽  
Vol 40 ◽  
pp. 80-83
Author(s):  
H.A. Cohen ◽  
T.W. Jeng ◽  
W. Chiu
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Three Dimensional ◽  
Data Interpretation ◽  
Two Dimensional ◽  
Biological Objects ◽  
Density Maps ◽  
Density Map ◽  
Complex Crystal ◽  
High Resolution Microscopy

This tutorial will discuss the methodology of low dose electron diffraction and imaging of crystalline biological objects, the problems of data interpretation for two-dimensional projected density maps of glucose embedded protein crystals, the factors to be considered in combining tilt data from three-dimensional crystals, and finally, the prospects of achieving a high resolution three-dimensional density map of a biological crystal. This methodology will be illustrated using two proteins under investigation in our laboratory, the T4 DNA helix destabilizing protein gp32*I and the crotoxin complex crystal.

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