An Empirical Comparison of Particle Swarm and Predator Prey Optimisation
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dissipative particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A Novel Hybrid PSO-BP Algorithm for Neural Network Training
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
An application of PSO technique for harmonic elimination in a PWM inverter
Applied Soft Computing
Expert Systems with Applications: An International Journal
Predicted modified PSO with time-varying accelerator coefficients
International Journal of Bio-Inspired Computation
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
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Two-layer particle swarm optimization with intelligent division of labor
Engineering Applications of Artificial Intelligence
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
Review: A parameter selection strategy for particle swarm optimization based on particle positions
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper introduces a novel particle swarm optimization (PSO) with random position to improve the global search ability of particle swarm optimization with linearly decreasing inertia weight (IWPSO). Standard particle swarm optimization and most of its derivations are easy to fall into local optimum of the problem by lacking of mutation in those operations. Inspired by the acceptance probability in simulated annealing algorithm, the random factors could be put in particle swarm optimization appropriately. Consequently, the concept of the mutation is introduced to the algorithm, and the global search ability would be improved. A particle swarm optimization with random position (RPPSO) is tested using seven benchmark functions with different dimensions and compared with four well-known derivations of particle swarm optimization. Experimental results show that the proposed particle swarm optimization could keep the diversity of particles, and have better global search performance.