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- W4387526864 abstract "Aiming at the shortcomings of Sparrow Search Algorithm (SSA), such as low accuracy, slow convergence speed, and inconsistent global search and local development ability in the iterative process, a Sparrow Search Algorithm (DESSA), which combines the mutualism search strategy and the hybrid differential evolution population fitness selection strategy, is proposed. By introducing the mutualism search strategy and fitness selection strategy for optimization, the convergence speed and accuracy of the algorithm are improved and the local and global search capabilities of the algorithm are balanced, the risk of premature convergence of the swarm intelligence algorithm is reduced, the oscillation phenomenon near the global optimal solution in the later period of the algorithm is avoided, and the convergence performance of the algorithm in the later period is further improved. This article selects ten benchmark test functions and compares the optimization differences between DESSA and other optimization algorithms from multiple perspectives. The results show that DESSA has faster iterative convergence speed, higher optimization accuracy, and better stability." @default.
- W4387526864 created "2023-10-12" @default.
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- W4387526864 date "2023-10-11" @default.
- W4387526864 modified "2023-10-12" @default.
- W4387526864 title "Improvement and application of sparrow search algorithm" @default.
- W4387526864 doi "https://doi.org/10.1117/12.3003805" @default.
- W4387526864 hasPublicationYear "2023" @default.
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