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Variable Contribution-Based Differential Evolution for Large-Scale Global Optimization

Published: 01 August 2024 Publication History

Abstract

Large-scale optimization problems bring significant challenges for existing evolutionary algorithms, as their search capability cannot efficiently balance the contribution of numerous dimensions. Differential evolution (DE), due to its simplicity and efficiency, has been successfully applied to large-scale optimization problems. However, in many conventional DEs, all variables are treated equally, regardless of their contribution to the overall function value. Therefore, this paper proposes a variable contribution-Based DE to explore the search space efficiently by variables with higher contribution. Specifically, a novel estimation method is introduced to quantify the accumulated contribution of variables by evaluating the fitness improvement achieved in each successful evolution. The contribution information is then utilized to reallocate computing resources among variables. In this way, more computing resources will be allocated to variables with higher contribution, which can significantly speed up the convergence. Comprehensive experiments are conducted on the large-scale optimization benchmarks CEC 2013 and four state-of-the-art evolutionary algorithms designed for large-scale optimization. The results indicate that the proposed algorithm achieves competitive results compared with the state-of-the-art algorithms.

References

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      cover image ACM Conferences
      GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2024
      2187 pages
      ISBN:9798400704956
      DOI:10.1145/3638530
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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      Published: 01 August 2024

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      1. differential evolution (DE)
      2. evolutionary computation
      3. large-scale optimization
      4. resource allocation

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