Adaptive Parameter Selcetoin of Quantum-behaved Particle Swarm Optimization on Global Lebvel
نویسندگان
چکیده
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 APSO algorithm with those of SPSO and original QPQSO by test the algorithms on several benchmark functions. The experiments results show that APSO algorithm outperforms due to its strong global search ability.
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