Particle swarm optimization(PSO)which has the general purpose optimization method received much attention in past years.In many studies

PSO has been successful in a variety of optimization problems.But the speed of convergence of standard PSO algorithm on high dimensional search space is unacceptable in practice.The concept of particle health was proposed

and gives an algorithm for particle health calculation in this paper.A new variation of PSO model proposed based on particle health（HPSO） can effectively reduce the probability of local optimum and enhance convergence speed especially for high dimensional search spaces.The proposed were tested by a variety of high-dimensional benchmark functions

and compared with standard PSO algorithm and decreasing inertia weight variation(WPSO). It was found that the application of these modifications resulted in significant gain in speed and efficiency.