关键词: 多目标进化算法，适应度函数，选择策略，收敛性

Abstract: It is scarce for literatures devoted to the multiobjective evolutionary algorithms (MOEAs) to systematically research on selection strategics, however, these strategics arc crucial to MOEAs for solving some multiobjective optimination problems successfully,as they not only guide th e process of search and determine the search directions,but also exert great effect on the convergence of MOEAs. With the unified framework, the paper first discussed how to construct an appropriate fitness function in multiobjective optimization problem, then, selection strategics were classified as six categories based on MOEA's selection mechanism and principle through systematically analyzing various MOEAs. As it is rare for expressing the operators of the MOEAs symbolized in most literatures, which is not conducive to comprehend them deeply. This paper described the principle and mechanism of each selection strategy symbolized and analyzed its advantages and weaknesses respectively. At last, the paper proved the convergence of MOEAs with certain features, and the process of proof has shown that it is reasonable to regard PKNOWN achieved from the final results of MOEAs as PTRUE or the approximated Pareto optimal set.

Key words: Multiobjective evolutionary algorithms, Fitness function, Selection strategy, Convergence