| 摘要: |
| 基于分解的超多目标进化算法是求解各类超多目标优化问题的主流方法, 其性能在很大程度上依赖于所采用参考向量与真实帕累托前沿面(Pareto front, PF)的匹配程度. 现有基于分解的超多目标进化算法尚难以同时有效处理各类PF不同的优化问题. 为此, 提出了一种基于PF曲率预估的超多目标进化算法(MaOEA-CE). 所提算法的核心包括两个方面, 首先基于对PF曲率的预估, 在每次迭代过程中生成不同的参考向量, 以渐进匹配不同类型问题的真实PF; 其次在环境选择过程中, 再基于预估的曲率选择合适的聚合函数对精英解进行挑选, 并对参考向量进行动态调整, 在维护种群多样性的同时提升种群的收敛性. 为验证MaOEA-CE的有效性, 将其与7个先进的超多目标算法在3个主流测试问题集DTLZ、WFG和MaF上进行对比, 实验结果表明MaOEA-CE具有明显的竞争力. |
| 关键词: 超多目标优化|进化算法|曲率预估|参考向量|环境选择 |
| DOI：10.13328/j.cnki.jos.006648 |
| 分类号: |
| 基金项目:国家重点研发计划(2021YFB2900800); 国家自然科学基金(61871272); 广东省自然科学基金(2020A1515010479, 2021A1515011911); 深圳市高等院校稳定支持(20200811181752003) |
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| Many-objective Evolutionary Algorithm Based on Curvature Estimation of Pareto Front |
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LIANG Zheng-Ping, LIN Wan-Peng, HU Kai-Feng, MING Zhong, ZHU Ze-Xuan
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College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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| Abstract: |
| The many-objective evolutionary algorithm based on decomposition is the main approach to solving many-objective optimization problems, but its performance largely depends on the matching degree between the adopted reference vectors and the real Pareto front (PF). Existing decomposition-based many-objective evolutionary algorithms can hardly deal with all kinds of many-objective optimization problems with different PF at the same time. To solve this issue, this study proposes a many-objective evolutionary algorithm based on the curvature estimation (MaOEA-CE) of PF. The core of the proposed algorithm includes two aspects: Firstly, on the basis of PF curvature estimation, different reference vectors are generated in each iteration to gradually match the real PF of different kinds of problems. Secondly, with the estimated PF curvature, the appropriate aggregation function is used to select elite solutions and dynamically adjust the generated reference vector in the environmental selection, which can improve the convergence while maintaining the diversity of the population. Moreover, MaOEA-CE is compared with seven advanced many-objective algorithms on three mainstream problem sets for testing, i.e., DTLZ, WFG, and MaF, to verify its effectiveness. The experimental results prove that MaOEA-CE has strong competitiveness. |
| Key words: many-objective optimization|evolutionary algorithm|curvature estimation|reference vectors|environmental selection |
