Escapable Hybrid Particle Swarm Optimization
Cao De-xin · Microelectronics & Computer · 2012
The particle swarm optimization algorithm has a few disadvantages in solving complex functions,including low solving precisions and high possibilities of being trapped in local optimum.According to genetics good parent often produce hybrid good offspring,an improved program is proposed,as the personal best position of the particle contains more useful information than the current position,in each iteration,crossover is operated to the best personal particle,a better position may be got,and when the particle swarm trap premature convergence,without destroying the existing population structure by using new bacterial feeding chemotaxis and all particles gradually get rid of the shackles of local optimum.Numerical examples show the effectiveness of the proposed algorithm.