Binary Information Press
Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Particle Swarm Optimization (PSO) algorithm has high convergence speed and is easy to implement, but it also exists precocious phenomenon. In the later stage of optimization, the improvement is not good and is easy to be trapped in local minima. To overcome this shortcoming, this paper will firstly introduce Cloud Adaptively model into Particle Swarm Optimization (CAPSO), so, It is to divide the particle into two parts, close to or away from the best particle, the former particles' weight of inertia will be adaptively adjusted by X-condition generator of cloud model then improve it with gradient theory.