Research On Particle Swarm Algorithms

Many problems possess a set of parameters to be optimized, especially in fields of engineering technology, scientific research and economic management. Optimization theory and its techniques will surely take more and more important part in the information era of 21 century. As a newly developed swarm intelligence paradigm, particle swarm algorithm is a very promising optimization tool, with many advantages in high-dimensional problems or tasks that lack prior knowledge. Its basic idea is originated from Social Psychology and Artificial Life as a simulation of socio-cognitive processes. Because of its high convergence rate and excellent generalization, particle swarm algorithm has attracted much attention since it was first proposed in 1995.In this literature, most researchers have focused their efforts on how to promote the convergence rate and avoid the premature convergence problem. Introducing new mechanisms to ensure the diversity of swarm population or escape from local minima may be useful on relieving premature convergence of the algorithm. As to improving convergence rate, much work focus on tuning strategy parameters, or modifying the original framework with ideas inspired from other meta-heuristics. As most researchers of this field are with pure scientific computing or engineering applications background, they care more about the results than probe into the real cause, not to...