Description: Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.)
This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. when a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest. Following is the steps of PSO:
- [SPTK-3.0] - using Matlab was a voice processing tool
- [LevelSet_ChunmingLi_v0] - This Matlab code implements an edge base
- [gpso] - Particle Swarm Optimization (PSO) is an
- [tracking] - particle filter for tracking visual targ
- [PSO-evolutionarycomputation] - Particle Swarm Optimization (PSO) is an
- [pso-down] - Python language used particle swarm opti
- [GA_main] - Genetic algorithm source code package, i
- [GA_BP] - First, the direct training of BP network
- [pso.py] - Particle swarm optimization (PSO) is a f
- [pso-ppt-sample] - Particle Swarm Optimization (PSO) is an
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PSO.m
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