  Two important characteristics of multi-objective evolutionary algorithms are
distribution and convergency. As a classic multi-objective genetic algorithm,
NSGA-II is widely used in multi-objective optimization fields. However, in
NSGA-II, the random population initialization and the strategy of population
maintenance based on distance cannot maintain the distribution or convergency
of the population well. To dispose these two deficiencies, this paper proposes
an improved algorithm, OTNSGA-II II, which has a better performance on
distribution and convergency. The new algorithm adopts orthogonal experiment,
which selects individuals in manner of a new discontinuing non-dominated
sorting and crowding distance, to produce the initial population. And a new
pruning strategy based on clustering is proposed to self-adaptively prunes
individuals with similar features and poor performance in non-dominated sorting
and crowding distance, or to individuals are far away from the Pareto Front
according to the degree of intra-class aggregation of clustering results. The
new pruning strategy makes population to converge to the Pareto Front more
easily and maintain the distribution of population. OTNSGA-II and NSGA-II are
compared on various types of test functions to verify the improvement of
OTNSGA-II in terms of distribution and convergency.
