Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Journal of Global Optimization
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
A new hybrid NM method and particle swarm algorithm for multimodal function optimization
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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
Incremental Particle Swarm-Guided Local Search for Continuous Optimization
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
In this article, we study hybrid Particle Swarm Optimization (PSO) algorithms for continuous optimization. The algorithms combine a PSO algorithm with either the Nelder-Mead-Simplex or Powell’s Direction-Set local search methods. Local search is applied each time the PSO part meets some convergence criterion. Our experimental results for test functions with up to 100 dimensions indicate that the usage of the iterative improvement algorithms can strongly improve PSO performance but also that the preferable choice of which local search algorithm to apply depends on the test function. The results also suggest that another main contribution of the local search is to make PSO algorithms more robust with respect to their parameter settings.