Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Dynamic-probabilistic particle swarms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Exposing origin-seeking bias in PSO
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
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 companion on Genetic and evolutionary computation
Understanding particle swarms through simplification: a study of recombinant PSO
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Simple Dynamic Particle Swarms without Velocity
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
| Hi-index | 0.00 |
Standard particle swarms exhibit both multiplicative and additive stochasticity in their update equations. Recently, a simpler particle swarm with just additive stochasticity has been proposed and studied using a new theoretical approach. In this paper we extend the main results of that study to a large number of existing particle swarm optimisers by defining a general update rule from which actual algorithms can be instantiated via the choice of specific recombination operators. In particular, we derive the stability conditions and the dynamic equations for the first two moments of the sampling distribution during stagnation, and show how they depend on the used recombination operator. Finally, the optimisation efficiency of several particle swarms with additive stochasticity is compared in a suite of 16 benchmark functions.