Adaptive Parameter Selection of Quantum-behaved Particle Swarm Optimization on Global Level
| Authors: | Sun Jun, Southern Yangtze University, China
Xu Wenbo, Southern Yangtze University, China
Feng Bin, Southern Yangtze University, China |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Genetic and Evolutionary Algorithms |
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| Keywords: | Adaptive algorithm, Global Optimisation, Swarm Intelligence, Multidimensional, Non-linear system, Optimisation Problem |
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Abstract
In this paper, we formulate the dynamics and philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the whole population level. After that, we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization Algorithm (AQPSO). We compare the performance of AQPSO algorithm with those of SPSO and original QPSO by testing the algorithms on several benchmark functions. The experiments results show that AQPSO algorithm outperforms due to its strong global search ability. Copyright © 2005 IFAC.
