Designing MIMO controller by neuro-traveling particle swarm optimizer approach
Expert Systems with Applications: An International Journal
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
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 01
Niching without niching parameters: particle swarm optimization using a ring topology
IEEE Transactions on Evolutionary Computation
Fuzzy particle swarm optimization clustering and its application to image clustering
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Adaptive particle swarm optimization for reactive power and voltage control in power systems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
Particle swarm optimization is a new globe optimization algorithm based on swarm intelligent search. It is a simple and efficient optimization algorithm. Therefore, this algorithm is widely used in solving the most complex problems. However, particle swarm optimization is easy to fall into local minima, defects and poor precision. As a result, an improved particle swarm optimization algorithm is proposed to deal with multi-modal function optimization in high dimension problems. Elite particles and bad particles are differentiated from the swarm in the initial iteration steps, bad particles are replaced with the same number of middle particles generated by mutating bad particles and elite particles. Therefore, the diversity of particle has been increased. In order to avoid the particles falling into the local optimum, the direction of the particles changes in accordance with a certain probability in the latter part of the iteration. The results of the simulation and comparison show that the improved PSO algorithm named EGAPSO is verified to be feasible and effective.