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中文摘要：
为了解决多目标进化算法中适应值指派（fitness assignment）的耗时问题,提出了一种新颖的适应值指派方法——占优树.占优树保存了个体之间的必要信息,暗含了个体的密度信息,而且显著减少了个体之间的比较.此外,基于占优树的淘汰策略没有花费额外的代价就保存了种群多样性.在此基础上,提出了一种新的基于占优树的多目标进化算法.通过6个测试问题和3个方面的测试标准,新算法在接近真实的最优前沿和保持种群的多样性方面,与SPEA2和NSGA-Ⅱ性能相当,但速度要比它们快得多.
英文摘要：
To solve the time-consuming problem of the fitness assignment in the multi-objective evolutionary algorithm, this paper proposes a novel fitness assignment-dominating tree. The dominating tree preserves the necessary relationships among individuals, contains the density information implicitly, and reduces the comparisons among individuals distinctly. In addition, a smart eliminating strategy based on the dominating tree maintains the diversity of the population without extra expenses. A new multi-objective evolutionary algorithm based on dominating tree is proposed on these innovations. By examining three performance metrics on six test problems, the new algorithm is found to be competitive with SPEA2 and NSGA-Ⅱ in terms of converging to the true Pareto front and maintaining the diversity of the population, moreover, it is much faster than other two algorithms.
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