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International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)

By
Chen Ming
Chen Ming
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ISBN:
9780791859902
No. of Pages:
1400
Publisher:
ASME Press
Publication date:
2011

Quantum particle swarm optimization, as a random search algorithm having quantum behavior based on the classical particle swarm optimization, , with is more powerful and less control over search parameters compared to particle swarm optimization. But each of the algorithms is easily trapped in local optimal value resulting prematurity. This article aims to increase diversity in search of particles, proposes a method to improve the QPSO by differential evolution and shows that the algorithm is superiority compared to particle swarm optimization and quantum particle swarm optimization by standard test functions.

Abstract
Keywords:
Introduction
Quantum-Behaved Particle Swarm Optimization
The Proposed Deferential Evolutionquantum- Behaved Particle Swarm Optimization
Simulation Experiments and Results' Constract
Conclusion
References
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