计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 56-62.doi: 10.11896/jsjkx.230100004

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于在线学习稀疏特征的大规模多目标进化算法

高梦琦1,2, 冯翔1,2, 虞慧群1,2, 王梦灵3   

  1. 1 华东理工大学计算机科学与工程系 上海200237
    2 上海智慧能源工程技术研究中心 上海200237
    3 华东理工大学自动化系 上海20023
  • 收稿日期:2022-12-30 修回日期:2023-06-25 出版日期:2024-03-15 发布日期:2024-03-13
  • 通讯作者: 冯翔(xfeng@ecust.edu.cn)
  • 作者简介:(y10190116@mail.ecust.edu.cn)
  • 基金资助:
    国家重点研发计划(2020YFB1711700);国家自然科学基金面上项目(62276097);国家自然科学基金重点项目(62136003);上海市经信委“信息化发展专项资金”(XX-XXFZ-02-20-2463);上海市科技创新行动计划(21002411000)

Large-scale Multi-objective Evolutionary Algorithm Based on Online Learning of Sparse Features

GAO Mengqi1,2, FENG Xiang1,2, YU Huiqun1,2, WANG Mengling3   

  1. 1 Department of Computer Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
    2 Shanghai Engineering Research Center of Smart Energy,Shanghai 200237,China3 Department of Automation,East China University of Science and Technology,Shanghai 200237,China
  • Received:2022-12-30 Revised:2023-06-25 Online:2024-03-15 Published:2024-03-13
  • About author:GAO Mengqi,born in 1994,postgra-duate.Her main research interests include swarm intelligence,machine learning and evolutionary computing.FENG Xiang,born in 1977,Ph.D,professor,is a member of CCF(No.16665M).Her main research interests include distributed swarm intelligence and evolutionary computing,integration learning and integration optimization,and big data intelligence.
  • Supported by:
    National Key Research and Development Program of China(2020YFB1711700), National Natural Science Foundation of China(62276097),Key Program of National Natural Science Foundation of China(62136003),Special Fund for Information Development of Shanghai Economic and Information Commission(XX-XXFZ-02-20-2463) and Scientific Research Program of Shanghazi Science and Technology Commission(21002411000).

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摘要： 大规模稀疏多目标优化问题(Sparse Multiobjective Optimization Problems,SMOPs)广泛存在于现实世界。为大规模SMOPs提出通用的解决方法,对于进化计算、控制论和机器学习等领域中的问题解决都具有推动作用。由于SMOPs具有高维决策空间和Pareto最优解稀疏的特性,现有的进化算法在解决SMOPs时,很容易陷入维数灾难的困境。针对这个问题,以稀疏分布的学习为切入点,提出了一种基于在线学习稀疏特征的大规模多目标进化算法(Large-scale Multiobjective Evolutio-nary Algorithm Based on Online Learning of Sparse Features,MOEA/OLSF)。具体地,首先设计了一种在线学习稀疏特征的方法来挖掘非零变量;然后提出了一种稀疏遗传算子,用于非零变量的进一步搜索和子代解的生成,在非零变量搜索过程中,其二进制交叉和变异算子也用于控制解的稀疏性和多样性。与最新的优秀算法在不同规模的测试问题上的对比结果表明,所提算法在收敛速度和性能方面均更优。

关键词: 进化算法, 大规模多目标优化, 稀疏Pareto最优解, 在线学习

Abstract: Large-scale sparse multiobjective optimization problems(SMOPs) are widespread in the real world.Proposing generic solutions for large-scale SMOPs can improve problem-solving in the fields of evolutionary computation,cybernetics,and machine learning.Due to the high-dimensional decision space and the sparse Pareto-optimal solutions of SMOPs,existing evolutionary algorithms are vulnerable to the curse of dimensionality when solving SMOPs.To address these problems,a large-scale multi-objective evolutionary algorithm based on online learning of sparse features(MOEA/OLSF) is proposed,with the learning of sparse distribution as an entry point.Specifically,an online learning sparse features method is designed to mine nonzero variables.Then a sparse genetic operator is proposed for further searching nonzero variables and generating offspring solutions.Its binary crossover and mutation operators are used to control the sparsity and diversity of solutions in the nonzero variable mining process.The comparison results with the state-of-the-art algorithms on test problems with different scales show that the proposed algorithm outperforms the existing algorithm in terms of convergence speed and performance.

Key words: Evolutionary algorithm, Large-scale multiobjective optimization, Sparse Pareto-optimal solutions, Online learning

中图分类号: 

  • TP181
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