The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Parameter selection and adaptation in Unified Particle Swarm Optimization
Mathematical and Computer Modelling: An International Journal
Control of dead-time systems using derivative free particle swarm optimisation
International Journal of Bio-Inspired Computation
Robust multi-user detection based on quantum bee colony optimisation
International Journal of Innovative Computing and Applications
International Journal of Wireless and Mobile Computing
Membrane quantum particle swarm optimisation for cognitive radio spectrum allocation
International Journal of Computer Applications in Technology
Using QIGSO with steepest gradient descent strategy to direct orbits of chaotic systems
International Journal of Computational Science and Engineering
Parallelism errors evaluation using a hybrid optimisation algorithm
International Journal of Computer Applications in Technology
Hi-index | 0.00 |
In order to improve the performance of particle swarm optimisation algorithm in the complicated function optimisation, a new improved measure was advanced. The new algorithm only memorised the individual information of finite steps in the iterations and utilised the average information of swarm. Due to the individuals forgetting the former best positions, the forgetting character was hold. The ability of exploration was improved because of using forgetting character and average information of swarm. The simulations of complicated function optimisation show that the new algorithm can find the global best solution more easily than the common particle swarm optimisation algorithm.