面向复杂超多目标优化问题的自适应增强学习进化算法
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燕山大学电气工程学院

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中图分类号:

TP18

基金项目:

国家自然科学基金(62003296);河北省自然科学基金(F2020203031);河北省高等学校科学技术研究项目(QN2020225)


Adaptive Boosting Learning Evolutionary Algorithm for Complex Many-objective Optimization Problems
Author:
Affiliation:

Institute of Electrical Engineering, Yanshan University

Fund Project:

National Natural Science Foundation of China(62003296);Natural Science Foundation of Hebei(F2020203031);Science and Technology Project of Hebei Education Department(QN2020225)

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    摘要:

    在解决超多目标优化问题中,基于分解的进化算法被证实是一种较为有效的方法。传统的分解方法依赖于一组均匀分布的参考向量,它借助聚合函数将多目标优化问题分解为一组的单目标子问题,然后对这些子问题同时进行优化。然而,由于参考向量分布和Pareto前沿形状的不一致性,导致这些预定义的参考向量在解决复杂超多目标优化问题时表现较差。针对以上问题,本文提出了一种基于自适应增强学习的超多目标进化算法(MaOEA-ABL)。该算法主要分为两个阶段:第一阶段采用一种自适应增强学习算法对预定义的参考向量进行调整,在学习过程中删除无用向量,增加新的向量;第二阶段设计一种对Pareto形状无偏好的分解方法。为验证算法的有效性,选取具有复杂Pareto前沿的MaF系列测试函数进行仿真研究,结果显示MaOEA-ABL算法的IGD均值在67%的测试函数上超过了对比算法,表明该算法在复杂超多目标优化问题中表现良好。

    Abstract:

    When dealing with many-objective optimization problems, the evolutionary algorithm based on decomposition is proved to be a effective method. The traditional decomposition relys on a set of uniformly distributed reference vector. This method decomposes the multi-objective optimization problem into a set of single-objective subproblems through aggregation functions, and then optimizes these subproblems simultaneously. However, these predefined reference vectors perform poorly in solving complex many-objective optimization problems because of the inconsistency of the distribution of reference vectors and the shape of the Pareto front. Aiming at the above problems, a many-objective evolutionary algorithm based on adaptive boosting learning (MaOEA-ABL) is proposed. The algorithm can be divided into two stages. In the first stage, an adaptive boosting learning algorithm is used to adjust the predefined reference vectors. In the learning process, useless vectors are deleted and new vectors are added. In the second stage, an unbiased decomposition method of Pareto shape is designed. Simulation has been conducted on MaF test problems. The experimental results show that the IGD mean value of MaOEA-ABL is better than that of the comparison algorithms in 67% of the test functions, which indicates that MaOEA-ABL performs well in many-objective optimization problems with complex Pareto front.

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历史
  • 收稿日期:2021-04-25
  • 最后修改日期:2021-08-17
  • 录用日期:2021-08-18
  • 在线发布日期: 2021-09-01
  • 出版日期: