Modified particle swarm optimization based on immune clone principle for analog-matching of equivalent system

Low order equivalent system of aircraft can be calculated and evaluated flying qualities of high order system. A modified particle swarm optimization based on immune clone principle is proposed aiming at analog-matching of equivalent system. Through a comprehensive analysis of PSO algorithm, immunity clone algorithm was introduced to the PSO algorithm based on traditional velocity-displacement operator. The antibodies were regard as particles, and the clone selection, clone suppression and high-frequency mutation based on real coding were performed according to the degree of affinity, which enhanced convergence speed and precision of the algorithm. In the meantime, the initialization based on entropy maximization was adopted to improve the diversity of initialized particle swarm. The results show that the advanced algorithm can converge to the global optimum at a great rate with non-impact of initial value, and has important significance in online evaluation of aircraft flying qualities.