Quantum-behaved Particle Swarm Optimization (QPSO) for Feature Selection
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Resource Overview
This MATLAB program implements Quantum-behaved Particle Swarm Optimization (QPSO) for feature selection, allowing users to specify optimization direction for objective functions, adjust population size, and modify the QPSO beta parameter internally. The implementation includes configurable parameters and modular structure for easy customization.
Detailed Documentation
This MATLAB program implements Quantum-behaved Particle Swarm Optimization (QPSO) for feature selection tasks. Users can configure the optimization direction (minimization/maximization) for objective functions, adjust population size parameters, and modify the QPSO algorithm's beta parameter within the codebase. The program architecture supports easy extension for additional functionalities, such as integrating comparative optimization algorithms or adding visualization capabilities for results analysis. Key implementation features include adaptive position updating using quantum behavior principles and fitness evaluation mechanisms for feature subset optimization. This flexible framework enables researchers to conduct customized feature selection experiments and enhance algorithm performance through parameter tuning and method comparisons.
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