Quasi-random Fractal Search (QRFS)

版本 1.0.0 (9.3 KB) 作者: Diego Oliva
Here is proposed the Quasi-random Fractal Search (QRFS): A dynamic metaheuristic with sigmoid population decrement for global optimization
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更新时间 2024/6/25

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This study introduces a new optimization approach called quasirandom metaheuristic based on fractal search (QRFS), which harnesses the power of fractal geometry, low discrepancy sequences, and intelligent search space partitioning techniques. The QRFS uses fractals’ inherent self-similarity and intricate structure to guide the solution space exploration. For the proposal, a deterministic but quasi-random element is used in the search process using low discrepancy sequences, such as Sobol, Halton, Hammersley, and Latin Hypercube. This integration allows the algorithm to systematically cover the search space while maintaining the level of diversity necessary for efficient exploration. The QRFS employs a dynamic strategy of partitioning the search space and reducing the population of solutions to optimize the use of function accesses, which causes it to adapt well to the characteristics of the problem. The algorithm intelligently identifies and prioritizes promising regions within the fractal-based representation, allocating computational resources where they are most likely to yield optimal solutions.

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Diego Oliva (2025). Quasi-random Fractal Search (QRFS) (https://www.mathworks.com/matlabcentral/fileexchange/168761-quasi-random-fractal-search-qrfs), MATLAB Central File Exchange. 检索时间: .

Beltran, Luis A., et al. “Quasi-Random Fractal Search (QRFS): A Dynamic Metaheuristic with Sigmoid Population Decrement for Global Optimization.” Expert Systems with Applications, vol. 254, Elsevier BV, Nov. 2024, p. 124400, doi:10.1016/j.eswa.2024.124400.

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