The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
The role of mutation and recombination in evolutionary algorithms
The role of mutation and recombination in evolutionary algorithms
Particle swarms and population diversity
Soft Computing - A Fusion of Foundations, Methodologies and Applications
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
Improved particle swarm optimizer based on adaptive random learning approach
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An analysis of heterogeneous cooperative algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
| Hi-index | 0.00 |
We apply a novel theoretical approach to better understand the behaviour of different types of bare-bones PSOs. It avoids many common but unrealistic assumptions often used in analyses of PSOs. Using finite element grid techniques, it builds a discrete Markov chain model of the BB-PSO which can approximate it on arbitrary continuous problems to any precision. Iterating the chain's transition matrix gives precise information about the behaviour of the BB-PSO at each generation, including the probability of it finding the global optimum or being deceived. The predictions of the model are remarkably accurate and explain the features of Cauchy, Gaussian and other sampling distributions.