Quantum Particle Swarm Optimization Algorithm Based on Dynamic Adaptive Search Strategy
Abstract: The particle swarm
system simulates the evolution of the social mechanism. In this system, the individual
particle representing the potential solution flies in the multidimensional
space in order to find the better or the optimal solution. But because of the
search path and limited speed, it's hard to avoid local best and the premature
phenomenon occurs easily. Based on the uncertain principle of the quantum mechanics,
the global search ability of the quantum particle swarm optimization (QPSO)
algorithms are better than the particle swarm optimization algorithm (PSO). On
the basis of the fundamental quantum PSO algorithm, this article introduces the
grouping optimization strategy, and meanwhile adopts thedynamic adjustment,
quantum mutation and possibility acceptance criteria to improve the global
searchcapability of the algorithm and avoid premature convergence phenomenon.
By optimizing the test functions, the experimental simulation shows that the
proposed algorithm has better global convergence and search ability.
Author: Jing Huo, Xiaoshu Ma
Journal Code: jptkomputergg150059
