Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
A fuzzy adaptive turbulent particle swarm optimisation
International Journal of Innovative Computing and Applications
Hybrid particle swarm optimization algorithm with fine tuning operators
International Journal of Bio-Inspired Computation
Predicted modified PSO with time-varying accelerator coefficients
International Journal of Bio-Inspired Computation
A study on the effect of vmax in particle swarm optimisation with high dimension
International Journal of Bio-Inspired Computation
Predicted-velocity particle swarm optimization using game-theoretic approach
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
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Classical particle swarm optimisation algorithms update the velocity and position of all dimensions at each iteration step, and accept the updated velocity and position unconditionally. For multi-dimensional function optimisation problems, this strategy deteriorates the intensification ability of algorithm because different dimensions may interfere with each other. To deal with this shortage, this paper presents a particle swarm optimisation algorithm with iterative improvement strategy for multi-dimensional function optimisation problems. In the process of search, each particle updates velocity and position dimension by dimension, and evaluates position dimension by dimension. In each dimension, if the updated value can improve the solution, it will be accepted. Otherwise, the original value is retained. In order to keep algorithm from premature stagnation, particle will accept the velocity and position, which are calculated using classical methods, if there is no improvement found in any dimension. The experiments, which were carried on benchmark functions, showed that the iterative improvement strategy can improve the performance of particle swarm optimisation algorithm remarkably.