Publication | Open Access
A New Implementation of Population Based Incremental Learning Method for Optimizations in Electromagnetics
33
Citations
6
References
2007
Year
Numerical AnalysisSearch OptimizationArtificial IntelligenceNew ImplementationEngineeringMachine LearningAvailable Pbil AlgorithmsIncremental LearningAlgorithmic LearningValue Function ApproximationIntelligent SystemsIncremental Learning MethodMemetic AlgorithmHybrid Optimization TechniqueComputational ElectromagneticsDifferential EvolutionElectrical EngineeringContinuous OptimizationIntelligent OptimizationAntennaComputer ScienceAdaptive AlgorithmSignal ProcessingProbability VectorsComputational ScienceAdaptive Updating Strategy
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm. </para>
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