Exploring the Effectiveness of Various Patterns in an Extended Pattern Search Layout Algorithm

Automated synthesis of product layout has the potential of substantially reducing design cycle time while allowing for quick check of interference, clearance, scale and fit prior to the building of physical prototypes. The search for optimal positions and orientations of parts in the layout typically requires a huge number of iterations. An extended pattern search layout algorithm based on coordinate search was introduced in an earlier paper and shown quite effective over the previous state-of-the-art. Coordinate search is a simple and straightforward way of implementing the extended pattern search method in the layout problem. However, it is not taking advantage of the wide variety of heuristics admissible in pattern search methods for identifying promising search directions. By introducing various search patterns and exploring their effectiveness in the layout problem, the question of whether complex tactics can do better than the basic coordinate pattern search is addressed. This paper presents four different heuristics for generating pattern directions in the extended pattern search layout algorithm: the conjugate direction method, the modified gradient method, the rank ordering method, and the simplex method. These heuristics are utilized to identify promising search directions and update the set of pattern directions used in the algorithm over iterations. The performance of the different heuristics is compared to that of the basic coordinate extended pattern search layout approach.

[1]  David C. Gossard,et al.  Reasoning on the Location of Components for Assembly Packaging , 1991 .

[2]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[3]  Carl Sechen,et al.  VLSI Placement and Global Routing Using Simulated Annealing , 1988 .

[4]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[5]  M. J. D. Powell,et al.  An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..

[6]  G. Sorkin Theory and practice of simulated annealing on special energy landscapes , 1992 .

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  W. Hills,et al.  A Layout Design System for Complex Made-to-order Products , 1996 .

[9]  P. Meehan,et al.  PLACEMENT OF SHAPEABLE BLOCKS , 1988 .

[10]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[11]  Jonathan Cagan,et al.  A Simulated Annealing-Based Approach to Three-Dimensional Component Packing , 1995 .

[12]  Georges M. Fadel,et al.  Bi-objective optimization of components packing using a genetic algorithm , 1996 .

[13]  J. Cagan,et al.  An Extended Pattern Search Algorithm for Three-Dimensional Component Layout , 2000 .

[14]  D. F. Wong,et al.  Simulated Annealing for VLSI Design , 1988 .

[15]  Jonathan Cagan,et al.  A simulated annealing-based algorithm using hierarchical models for general three-dimensional component layout , 1998, Comput. Aided Des..

[16]  Simon Szykman,et al.  An Integrated Approach to Optimal Three Dimensional Layout and Routing , 1998 .

[17]  Huang,et al.  AN EFFICIENT GENERAL COOLING SCHEDULE FOR SIMULATED ANNEALING , 1986 .

[18]  Simon Szykman,et al.  Constrained Three-Dimensional Component Layout Using Simulated Annealing , 1997 .

[19]  Simon Szykman,et al.  Synthesis of Optimal Nonorthogonal Routes , 1996 .

[20]  R. Balling,et al.  Optimal packaging of complex parametric solids according to mass property criteria , 1994 .