Dynamic-probabilistic particle swarms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Particle Swarm Optimization with Discrete Recombination: An Online Optimizer for Evolvable Hardware
AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
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The standard particle swarm optimiser uses update rules including both multiplicative randomness and velocity. In this paper, we look into a general particle swarm model that removes these two features, and study it mathematically. We derive the recursions and fixed points for the first four moments of the sampling distribution, and analyse the transient behaviour of the mean and the variance. Then we define actual instances of the algorithm by coupling the general update rule with specific recombination operators, and empirically test their optimisation efficiency.