QPSOQuantum Particle Swarm Optimization
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In this paper, to optimize the positioning algorithm, we propose a novel localization algorithm based on EHP and QPSO in the realm of multi-hop range-free localization schemes.
For the antenna pattern synthesis, we considered a new optimization algorithm based on the QPSO.
We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the above three objective functions.
In order to follow this trend and enhance the capabilities of a standard QPSO, the MapReduce quantum-behaved particle swarm optimization is developed.
Quantum particle swarm optimization (denoted as QPSO) is developed by analyzing the convergence of particle swarm optimization and quantum system.
Our work is also inspired by the quantum-behaved particle swarm optimization (QPSO) method in [11].
QPSO is used to iteratively tune the attention parameters [[lambda].sub.i], i = 1, ..., k where k is the number of classes.
Quantum particle swarm optimization (QPSO) which combines PSO with quantum computing theory is a novel swarm intelligence algorithm, which has a better performance for multi-relay selection problem [12].
introduce quantum theory into PSO and put forward a quantum-behaved PSO (QPSO) algorithm, which outperforms PSO in search ability and has fewer parameters to control.
[34] developed a novel variant of PSO called Quantum-behaved Particle Swarm Optimization (QPSO), where a strategy based on a quantum S potential well is employed to sample around the personal best points and then introduced the mean best position into the algorithm [35-37].
proposed a quantum-behaved PSO (QPSO) algorithm, which can be guaranteed theoretically to find optimal solution in search space.
proposed a new probabilistic algorithm, quantum-behaved particle swarm optimization (QPSO) algorithm [11].