A global search strategy of quantum-behaved particle swarm optimization

By Jun Sun, Wenbo Xu, Bin Feng. International Conference on Computational Intelligence and Security, 2004

781 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/6ac366c7394c384087b8720f887efc80148fcc5c

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Citations in the corpus

A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “Using another approach, Sun et al. (2004a, b) introduced quantum theory into PSO and proposed a quantum-behaved PSO (QPSO) algorithm, which can be guaranteed theoretically to find optimal solutions in a search space, and Marinakis et al. (2008) introduced an improved quantum-behaved particle swarm o”
Particle Swarm Methods (2018, neutral): “Three established approaches are the following [81, 133, 135],”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, background): “tain degree. There are however, several major shortcomings of the basic PSO that introduce failure modes such as stagnation and convergence to local optima which has led to extensive studies (such as [4, 5]) aimed at mitigation and resolution of the same. In this review, the foundations and frontier”
Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, methodology): “Later, a global point known as the mean best position was introduced into the algorithm in order to enhance the global search ability of the QPSO algorithm ( Sun et al., 2004b, 2005 ).”
A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, methodology): “Since the computational complexity of the MRPSO is the same as the PSO and the QPSO but simpler than other hybrid algorithms, we first observed the performance of the MRPSO in terms of quality of the average optimum value and the standard deviation for 50 trials, in comparison with the PSO [23] and ”
Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects (2008, methodology): “The setup of QPSO and HQPSO approaches used was the following: QPSO: QPSO proposed in [21,22] using a constant contraction– expansion coefficient given by b = 0.”