Improved QPSO algorithm based on random evaluation and its parameter control

Abstract

In order to improve the convergence performance of Quantum-behaved Particle Swarm Optimization(QPSO) algorithm,this paper proposed an improved QPSO algorithm which was called RE-QPSO based on the random evaluation strategy.The new algorithm evaluated the innovation of particles by using a random factor and improved the ability of the particles to get rid of the local optima.Fixed value strategy and linear decreasing strategy were proposed for controlingthe theunique parameter of QPSO algorithm and they were tested on six benchmark functions.According to the test results,some conclusions concerning the selection of the parameter were drawn.