Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Journal of Global Optimization
Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Locust swarms: a new multi-optima search technique
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Heterogeneous particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Selection strategies for initial positions and initial velocities in multi-optima particle swarms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Particle swarm optimization cannot guarantee convergence to the global optimum on multi-modal functions, so multiple swarms can be useful. One means to coordinate these swarms is to use a separate search mechanism to identify different regions of the solution space for each swarm to explore. The expectation is that these independent sub-swarms can each perform an effective search around the region where it is initialized. This regional focus means that sub-swarms will have different goals and features when compared to standard (single) swarms. A comprehensive study of these differences leads to a new set of general guidelines for the configuration of sub-swarms in multi-swarm systems.