On the moments of the sampling distribution of particle swarm optimisers
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Markov chain models of bare-bones particle swarm optimizers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Dynamics and stability of the sampling distribution of particle swarm optimisers via moment analysis
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Merging Groups for the Exploration of Complex State Spaces in the CPSO Approach
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Mean and variance of the sampling distribution of particle swarm optimizers during stagnation
IEEE Transactions on Evolutionary Computation
Cellular PSO: A PSO for Dynamic Environments
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
A high throughput system for intelligent watermarking of bi-tonal images
Applied Soft Computing
Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Scalability study of particle swarm optimizers in dynamic environments
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
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The optimisation of dynamic optima can be a difficult problem for evolutionary algorithms due to diversity loss. However, another population based search technique, particle swarm optimisation, is well suited to this problem. If some or all of the particles are ‘charged’, an extended swarm can be maintained, and dynamic optimisation is possible with a simple algorithm. Charged particle swarms are based on an electrostatic analogy—inter-particle repulsions enable charged particles to swarm around a nucleus of neutral particles. This paper proposes a diversity measure and examines its time development for charged and neutral swarms. These results facilitate predictions for optima tracking given knowledge of the amount of dynamism. A number of experiments test these predictions and demonstrate the efficacy of charged particle swarms in a simple dynamic environment.