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Master-slave model-based parallel chaos optimization algorithm for parameter identification problems
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DOI：10.1007/s11071-015-2443-0.png)
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摘要
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The parameter identification problem can be formalized as a multi-dimensional optimization problem, where an objective function is established minimizing the error between the estimated and measured data. In this article, a master-slave model (MSM)-based parallel chaos optimization algorithm (PCOA) (denoted as MSM-PCOA) is proposed for parameter identification problems. The MSM-PCOA is a novel global optimization algorithm, where twice carrier wave chaos search is employed as the master model, while the migration and crossover operation are used as the slave model. The MSM-PCOA is applied to the parameter identification of two different complex systems: bidirectional inductive power transfer system and chaotic systems. Simulation results, compared with other optimization algorithms, show that MSM-PCOA has better parameter identification performance.
Keyword:
Parameter identification
Optimization algorithm
Chaos
Parallel chaos optimization algorithm (PCOA)
Master-slave model
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期刊
IF:
6
论文数:
1.4W
被引数:
4.1W
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