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Based on the discussing of Quantum-Behaved Particle Swarm Optimization with Binary Encoding (BQPSO) , changed parameter is introduced into algorithm to make the progress of evolution more reasonable ,and multi-point crossover operator is used in computing the coordinate of the local attractor to increasing the diversity of populations and improve the global searching ability ,a modified BQPSO algorithm...
The random selection of optimal individual is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, and a linear weight parameter which implied the importance of particles in population according to their fitness value is used in QPSO to balance the global and local searching abilities while having better convergence...
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