Application of immune genetic algorithm based on autocorrelation theory in GPS ambiguity solution

摘要

The complex autocorrelation in the combinatorial optimization process of genetic algorithm usually makes it difficult to find a good solution. In this paper an optimization algorithm based on the autocorrelation theory is proposed to simulate the natural process of the population change, which includes the following steps: (1) initialization of the populations; (2) Computation of the fitness of the population and the individual; (3) immumegenetic manipulation; (4) and autocorrelation-based population adjustment. The natural mechanism of population change caused by the autocorrelation of individuals is simulated in a parallel way, therefore the algorithm can produce and update the good optimum until the best on appears. The experimental result shows that the proposed algorithm can find the better optimum and more intelligent than the common IGA at the same time.