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

An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy