DocumentCode :
1428862
Title :
Shape Optimization of Multistage Depressed Collectors by Parallel Evolutionary Algorithm
Author :
Coco, Salvatore ; Laudani, Antonino ; Pulcini, Giuseppe ; Fulginei, Francesco Riganti ; Salvini, Alessandro
Author_Institution :
DIEEI, Univ. of Catania, Catania, Italy
Volume :
48
Issue :
2
fYear :
2012
Firstpage :
435
Lastpage :
438
Abstract :
In this paper a novel parallel meta-heuristic algorithm called MeTEO is presented, applied to the shape optimization of multistage depressed collectors, simulated by means of a Finite Element collector and electron gun simulator, COLLGUN, which uses the Constructive Solid Geometry for the description of the device shape. METEO is a hybrid algorithm composed by three different heuristics: FSO (Flock of Starlings Optimization), PSO (Particle Swarm Optimization), and BCA (Bacterial Chemotaxis Algorithm); it performs the optimization using both the topological and the metric rules and offers a natural parallel implementation that allows speeding up the whole process of optimization by the fitness modification (FM).
Keywords :
evolutionary computation; parallel algorithms; particle swarm optimisation; COLLGUN; METEO; Starlings optimization; bacterial chemotaxis algorithm; constructive solid geometry; device shape; electron gun simulator; finite element collector; fitness modification; flock; hybrid algorithm; metric rules; multistage depressed collectors; parallel evolutionary algorithm; parallel meta-heuristic algorithm; particle swarm optimization; shape optimization; Electrodes; Electromagnetics; Geometry; Iron; Mathematical model; Optimization; Shape; Evolutionary computation; finite element methods; multistage depressed collector; optimization methods;
fLanguage :
English
Journal_Title :
Magnetics, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9464
Type :
jour
DOI :
10.1109/TMAG.2011.2174035
Filename :
6136743
Link To Document :
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