The Performance Study of Two Genetic Algorithm Approaches for VLSI Macro-Cell Layout Area Optimization

2008 ◽  
Author(s):  
H.A. Rahim ◽  
Ab Al Hadi Ab Rahman ◽  
R. Badlishah Ahmad ◽  
Wan Nur Suryani Firuz Wan Ariffin ◽  
Muhammad Imran Ahmad
Keyword(s):  
Genetic Algorithm ◽  
Performance Study ◽  
Macro Cell ◽  
Area Optimization ◽  
Layout Area

A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization

2012 ◽  
Vol 622-623 ◽  
pp. 64-68 ◽  
Author(s):  
S. Padmanabhan ◽  
M. Chandrasekaran ◽  
P. Asokan ◽  
V. Srinivasa Raman
Keyword(s):  
Genetic Algorithm ◽  
Power Efficiency ◽  
Mechanical Design ◽  
Design Solution ◽  
Functional Requirements ◽  
Performance Study ◽  
Gear Design ◽  
Adverse Result ◽  
Real Coded Genetic Algorithm ◽  
Comparative Results

he major problem that deals with practical engineers is the mechanical design and creativeness. Mechanical design can be defined as the choice of materials and geometry, which satisfies, specified functional requirements of that design. A good design has to minimize the most significant adverse result and to maximize the most significant desirable result. An evolutionary algorithm offers efficient ways of creating and comparing a new design solution in order to complete an optimal design. In this paper a type of Genetic Algorithm, Real Coded Genetic Algorithm (RCGA) is used to optimize the design of helical gear pair and a combined objective function with maximizes the Power, Efficiency and minimizes the overall Weight, Centre distance. The performance of the proposed algorithms is validated through LINGO Software and the comparative results are analyzed.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

Macro-cell placement for analog physical designs using a hybrid genetic algorithm with simulated annealing

2005 ◽  
Vol 12 (4) ◽  
pp. 379-396 ◽  
Author(s):  
Lihong Zhang ◽  
Rabin Raut ◽  
Yingtao Jiang ◽  
Ulrich Kleine ◽  
Yoohwan Kim
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Genetic Algorithm ◽  
Macro Cell ◽  
Cell Placement
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