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A brain information decomposition mechanism inspired evolutionary algorithm for large-scale multi-objective optimization
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AI
DOI：10.1007/s10489-025-07001-3.png)
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Abstract
En 中文
The high-dimensional decision space of large-scale multi-objective optimization poses challenges to evolutionary algorithms, which leads to potential trapping in local optima. This paper proposes a brain information decomposition mechanism inspired large-scale multi-objective evolutionary algorithm (IDLMEA). Three strategies are developed. 1) The redundant information subpopulation division strategy selects high-quality solutions using shift-based density estimation, which determines the global optimal information. 2) The synergistic information subpopulation division strategy identifies exploration direction information by dividing solutions using local sensitive hashing. 3) The redundant and synergistic information-based reproduction strategy generates offspring for effective exploration of the search space. The Friedman test values of IDLMEA for inverted generational distance on LSMOP and IMF benchmarks outperform ten competitors by at least 25.64% and 4.04%, respectively.
Keywords:
Large-scale multi-objective optimization
Brain information decomposition
Redundant information
Synergistic information
Information-based reproduction
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7.5K
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