Title :
Improvement on Parallel AQPSO Using the Best Position
Author :
Ma, Yun ; Liu, Yang ; Yang, Deyun ; Chen, Yuping
Author_Institution :
Coll. of Inf. Technol., TaiShan Univ., Tai´´an
Abstract :
Quantum-behaved Particle Swarm Optimization (QPSO) is a new particle swarm optimization (PSO) algorithm. Compared with standard PSO (SPSO), it guarantees that particles converge in global optimum point in probability and this algorithm has better performance and stability. This paper introduces an improved Adaptive QPSO algorithm, puts the parallelisms crude of AQPSO and high speed of computer together, and island model is introduced. Multiswarm Parallel AQPSO (PAQPSO) Algorithm is reported. The algorithm employs the co-evolution model to avoid pre-maturity and improves global search performance. This approach is tested on several accredited benchmark functions and the experiment results show much advantage of PAQPSO to PSOs, and the running time is also decreased in linear.
Keywords :
parallel algorithms; particle swarm optimisation; probability; quantum computing; search problems; co-evolution model; global search performance; multiswarm parallel adaptive QPSO algorithm; probability; quantum-behaved particle swarm optimization; Conferences; Data mining; Co-evolution; QPSO; adaptive; parallel;
Conference_Titel :
Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on
Conference_Location :
Moscow
Print_ISBN :
978-0-7695-3543-2
DOI :
10.1109/WKDD.2009.145
