Improved Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) is a new random computational method for tackling optimization functions. However, it is easily trapped into the local optimum when solving the complexity and high-dimensional problems, which makes the performance of PSO greatly reduced. To overcome this shortcoming, the paper proposes an Improved Particle Swarm Optimization (IPSO), by adding the third particle of having a more room for progress to guide the current particles´ velocity updating rule, Which can keep the diversity of the particles and reduce the probability of trapping into the local optimization .Besides, the program enhances and improves the stability and the convergence speed of the algorithm according to adjusting the particles which go beyond the default position space in each interiors. Five benchmark functions are tested, and the results indicate the effectiveness of the new program.