Genetic algorithm procedure for linear array failure correction

2000 ◽  
Vol 36 (3) ◽  
pp. 196 ◽  
Author(s):  
J.A. Rodríguez ◽  
F. Ares ◽  
E. Moreno ◽  
G. Franceschetti
Author(s):  
G C Onwubolu

This paper presents a new approach to the scheduling of manufacturing cells which have flow-shop configuration. The approach is based on the genetic algorithm, which is a meta-heuristic for solving combinatorial optimization problems. The performance measure demonstrated in this paper is the optimization of the mean flow time. The procedure developed automatically computes the make-span. A flexible manufacturing cell schedule is used as a case study. The genetic algorithm procedure was used to solve a published data set for simple scheduling problems. The genetic algorithm procedure was further used to solve large flow-shop scheduling problems having machine sizes of up to 30 and job sizes of up to 100 in very reasonable computation time. The results show that the genetic-algorithm-based heuristic is promising for scheduling manufacturing cells.


2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Sarayoot Todnatee ◽  
Chuwong Phongcharoenpanich

This research has proposed the iterative genetic algorithm (GA) optimization scheme to synthesize the radiation pattern of an aperiodic (nonuniform) linear array antenna. The aim of the iterative optimization is to achieve a radiation pattern with a side lobe level (SLL) of ≤−20 dB. In the optimization, the proposed scheme iteratively optimizes the array range (spacing) and the number of array elements, whereby the array element with the lowest absolute complex weight coefficient is first removed and then the second lowest and so on. The removal (the element reduction) is terminated once the SLL is greater than −20 dB (>−20 dB) and the elemental increment mechanism is triggered. The results indicate that the proposed iterative GA optimization scheme is applicable to the nonuniform linear array antenna and also is capable of synthesizing the radiation pattern with SLL ≤ −20 dB.


2017 ◽  
Vol 26 (4) ◽  
pp. 1048-1059 ◽  
Author(s):  
C. Han ◽  
L. Wang ◽  
Z. Zhang ◽  
J. Xie ◽  
Z. Xing

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