A bloch sphere quantum genetic algorithm for locomotive secondary spring load adjustment based on global-local mutation

Locomotive secondary spring load adjustment requires an efficient algorithm during shimming process. A quantum genetic algorithm based on Bloch spherical coordinates and global-local mutation operation (GLBQGA) is thus proposed in this paper. To increase population diversity, it encodes chromosomes...

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Bibliographic Details
Published in2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES) pp. 195 - 200
Main Authors Jiacai Li, Tianzhe Bao, Kun Han
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Algorithm design and analysis
Biological cells
Genetic algorithms
global-local mutation
Load modeling
Mathematical model
Optimization
quantum genetic algorithm
secondary spring load adjustment
Springs
uniform design
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Summary:Locomotive secondary spring load adjustment requires an efficient algorithm during shimming process. A quantum genetic algorithm based on Bloch spherical coordinates and global-local mutation operation (GLBQGA) is thus proposed in this paper. To increase population diversity, it encodes chromosomes with qubits in the coordinate of Bloch sphere. For a faster running speed, ranges of ankle parameters are reduced reasonably. To fully utilize information of the whole solution space and remained individuals, an operation combining global search with local search is designed to substitute the standard non-gate for mutation. Moreover, the uniform design technique is applied herein to avoid the blindness in parameters selection of this algorithm. Experimental results illustrate that GLBQGA has much more superiority in global optimization than traditional genetic algorithms and maintains quite a high performance in secondary spring load adjustment.
DOI:10.1109/INES.2016.7555119