Particle swarm optimizer with C-Pg mutation

This paper presents a modified PSO algorithm, called the PSO with C-Pg mutation, or PSOWC-Pg, the algorithm adopts C-Pg mutation, the idea is to replace global optimal point gBest with disturbing point C and gBest alternately in the original formulae, the probability of using C is R. There are two methods for selecting C: stochastic method and the worst fitness method. The stochastic method selects some particle's current position x or pBest as C stochastically in each iteration loop, the worst fitness method selects the worst particle's x or the pBest of some particle with the worst fitness value as C. So, when R is small enough, the distance between C and gBest will tend towards 0, particle swarm will converge slowly and irregularly. The results of experiments show that PSOWC-Pg exhibit excellent performance for test functions.