Abstract: The intrinsic random errors on the evolutionary operators and the pressure of selection and the noise of seleclion in the evolutionary process easily make the loss of diversity on the evolutionary population. But the maintenance of diversity on the population is very important because it is not only benificial to the search process but also becomes the essential objective in multiobjective optimization. With the unified framework, this paper first classifiesd the diversity strategy on the MOEAs and described the principles and mechanisms on different types of diversity strategies and analyzed their characteristics. Then this paper analyzed the complexity of these diversity operators. At last, this paper proved the convergence of MOEAs in the general sense and pointed out that it is necessary to keep the monotonicity in the evolutionary population and avoid the degradation of population as the design of new diversity strategy.

Key words: Multio均ective evolutionary algorithms, Diversity strategics, Complexity, Convergence