Abstract
Particle Swarm Optimization (PSO) is a new optimization algorithm, which is applied in many fields widely. But the original PSO is likely to cause the local optimization with premature convergence phenomenon. By using the idea of simulated annealing algo-rithm, we propose a modified algorithm which makes the most optimal particle of every time of iteration evolving continu-ously, and assign the worst particle with a new value to increase its disturbance. By the testing of three classic testing functions, we conclude the modified PSO algorithm has the better performance of convergence and global searching than the original PSO.
Highlights
Particle Swarm Optimization (PSO) algorithm is a new intelligent optimization algorithm intimating the bird swarm behaviors, which was proposed by psychologist Kennedy and Dr Eberhart in 1995 [1]
Compared with other optimization algorithms, the PSO is more objective and to perform well, it is applied in many fields such as the function optimization, the neural network training, the fuzzy system control, etc
In PSO algorithm, particles would lost the ability to explore new domains when they are searching in solution space, that is to say it will entrap in local optimization and causes the premature phenomenon
Summary
PSO algorithm is a new intelligent optimization algorithm intimating the bird swarm behaviors, which was proposed by psychologist Kennedy and Dr Eberhart in 1995 [1]. The particles search in the solution space following the best particle by changing their positions and the fitness frequently, the flying direction and velocity are determined by the objective function. In PSO algorithm, particles would lost the ability to explore new domains when they are searching in solution space, that is to say it will entrap in local optimization and causes the premature phenomenon. It is very import for PSO algorithm to be guaranteed to converge to the global optimal solution, and many modify PSO algorithms were researched in recent ten years.
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