Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Solving multi-period financial planning problem via quantum-behaved particle swarm algorithm
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Quantum-Behaved particle swarm optimization with adaptive mutation operator
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Quantum-Behaved particle swarm optimization clustering algorithm
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Improving quantum-behaved particle swarm optimization by simulated annealing
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Parameter selection of quantum-behaved particle swarm optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Adaptive parameter selection of quantum-behaved particle swarm optimization on global level
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Convergence analysis and improvements of quantum-behaved particle swarm optimization
Information Sciences: an International Journal
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In this paper, we propose a local quantum-behaved particle swarm optimization (LQPSO) as a generalized local search operator. The LQPSO is incorporated into a main quantum-behaved particle swarm optimization (QPSO), which leads to a hybrid QPSO scheme QPSO-LQPSO, with enhanced searching qualities. The main QPSO performs global exploration search while the LQSPO exploits a neighborhood of the current solution provided by the main QPSO to search better solutions. The proposed QPSO-LQPSO scheme is tested on a test set. Simulation results demonstrate the efficiency of the proposed QPSO-LQPSO scheme. For the same number of fitness evaluations, QPSO-LQPSO exhibited a significantly better performance than other particle swarm optimization algorithms.