Analysis and operation of three different forms probabilistic particle swarm optimization algorithm
Tao Sun, Minghai Xu · IOP Conference Series Earth and Environmental Science · 2017
Quantum-behaved Particle Swarm algorithm(QPSO) is a kind of probabilistic PSO algorithm based on quantum theory, which has been successfully applied to solve many optimization problems. This paper analyzes the conditions of the probability density function should satisfy in QPSO, and constructs three functions that meet the requirements: exponential form, normal form and power form; thus obtains three position equation of particle by using the stochastic simulation method; then compares the convergence of the three forms PSO algorithm, and uses different types of standard test functions to evaluate them. The results show that exponential form and power form PSO has better convergence speed and calculation accuracy than standard PSO. In three different forms algorithm, power form PSO has better global search ability, and more suitable for solving high-dimensional and Multi-extremum optimization problem.