Titlebook: Advances in Swarm Intelligence; First International Ying Tan,Yuhui Shi,Kay Chen Tan Conference proceedings 2010 The Editor(s) (if applicab

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Grouping-Shuffling Particle Swarm Optimization: An Improved PSO for Continuous Optimizationswarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evolutionary process. By testing on 4 benchmark function

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An Improved Probability Particle Swarm Optimization Algorithmwo normal distributions are used to describe the distributions of particle positions, respectively. One is the normal distribution with the global best position as mean value and the difference between the current fitness and the global best fitness as standard deviation while another is the distrib

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Improved Quantum Particle Swarm Optimization by Bloch Spheree Swarm Optimization (PSO). QPSO performs better than normal PSO on several benchmark problems. However, QPSO’s quantum bit(Qubit) is still in Hilbert space’s unit circle with only one variable, so the quantum properties have been undermined to a large extent. In this paper, the Bloch Sphere encodin

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