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| 一种快速的基于占优树的多目标进化算法 |
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石川1,2, 李清勇1,2,3, 史忠植1
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1.中国科学院,计算技术研究所,智能信息处理重点实验室,北京,100080;2.中国科学院,研究生院,北京,100049;3.北京交通大学,计算机与信息技术学院,北京,100044
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| 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. |
| 关键词: 多目标进化算法 进化算法 占优树 淘汰策略 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60435010(国家自然科学基金);the National Grand Fundamental Research 973 Program of China under Grant No.2003CB317004(国家重点基础研究发展规划(973));the Natural Science Foundation of Beijing of China |
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| A Quick Multi-Objective Evolutionary Algorithm Based on Dominating Tree |
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SHI Chuan,LI Qing-Yong,SHI Zhong-Zhi
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| Abstract: |
| 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. |
| Key words: multi-objective evolutionary algorithm evolutionary algorithm dominating tree eliminating strategy |
