Diversity guided immune clonal quantum-behaved particle swarm optimization algorithm and the wavelet in the forecasting of foundation settlement
In dealing with the problem of the quantum-behaved particle swarm optimization algorithm (QPSO) easy falling into the local optima, we proposed the diversity guided immune clonal QPSO. In this algorithm the swarm was defined two states: attraction and expansion. During the optimization process the swarm transferred between the two states repeatedly reference to the swarm diversity. When in the attraction state if the diversity is less than the pre-established value, we will carry the immune clonal algorithm to do the local searching. And we used this algorithm with wavelet to forecast the foundation settlement, and also made a compare with standard quantum-behaved particle swarm optimization with wavelet. The experiment indicated that this improved method had a better ability of searching global and local optima and high forecasting precision.