Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 56-62.doi: 10.11896/jsjkx.230100004

• Database & Big Data & Data Science • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP181
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