随机搜索 (RS)

class pypop7.optimizers.rs.rs.RS(problem, options)[源代码]

随机（stochastic）搜索（优化）（RS）。

这是所有 RS 类的抽象类。请使用其任何实例化的子类来优化手头的黑盒问题。

注意

“局部搜索在 1990 年代初因其在大型（组合）问题上出人意料的良好结果……以及引入了随机性、多重同步搜索和其他改进而重新焕发了活力。”——[Russell&Norvig, 2022, AIMA]

随机局部搜索 (RLS) 通常被视为`启发式优化算法之一，也称为爬山法、最速上升法或贪婪搜索<>`_。

参数:

  • problem (dict) –

    问题参数，包含以下通用设置 (键)

    • 'fitness_function' - 需要被最小化的目标函数 (func),

    • 'ndim_problem' - 维度数量 (int),

    • 'upper_boundary' - 搜索范围的上边界 (array_like),

    • 'lower_boundary' - 搜索范围的下边界 (array_like).

  • options (dict) –

    优化器选项，包含以下通用设置 (键)

    • 'max_function_evaluations' - 函数评估的最大次数 (int, 默认: np.inf),

    • 'max_runtime' - 允许的最大运行时间 (float, 默认: np.inf),

    • 'seed_rng' - 随机数生成器的种子，需要明确设置 (int);

    以及以下特定设置 (key)

    • “x” - 初始（起始）点（array_like）。

x

初始（起始）点。

类型:

array_like

参考文献

Nesterov, Y. and Spokoiny, V., 2017. Random gradient-free minimization of convex functions. Foundations of Computational Mathematics, 17(2), pp.527-566. https://link.springer.com/article/10.1007/s10208-015-9296-2

Bergstra, J. and Bengio, Y., 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2). https://www.jmlr.org/papers/v13/bergstra12a.html

Appel, M.J., Labarre, R. and Radulovic, D., 2004. On accelerated random search. SIAM Journal on Optimization, 14(3), pp.708-731. https://epubs.siam.org/doi/abs/10.1137/S105262340240063X

Schmidhuber, J., Hochreiter, S. and Bengio, Y., 2001. Evaluating benchmark problems by random guessing. A Field Guide to Dynamical Recurrent Networks, pp.231-235. https://ml.jku.at/publications/older/ch9.pdf

Schmidhuber, J. and Hochreiter, S., 1996. Guessing can outperform many long time lag algorithms. Technical Report. https://www.bioinf.jku.at/publications/older/3204.pdf

Rastrigin, L.A., 1986. Random search as a method for optimization and adaptation. In Stochastic Optimization. https://link.springer.com/chapter/10.1007/BFb0007129

Solis, F.J. and Wets, R.J.B., 1981. Minimization by random search techniques. Mathematics of Operations Research, 6(1), pp.19-30. https://pubsonline.informs.org/doi/abs/10.1287/moor.6.1.19

Schrack, G. and Choit, M., 1976. Optimized relative step size random searches. Mathematical Programming, 10(1), pp.230-244. https://link.springer.com/article/10.1007/BF01580669

Schumer, M.A. and Steiglitz, K., 1968. Adaptive step size random search. IEEE Transactions on Automatic Control, 13(3), pp.270-276. https://ieeexplore.ieee.org/abstract/document/1098903

Matyas, J., 1965. Random optimization. Automation and Remote control, 26(2), pp.246-253.

Karnopp, D.C., 1963. Random search techniques for optimization problems. Automatica, 1(2-3), pp.111-121. https://www.sciencedirect.com/science/article/abs/pii/0005109863900189

Rastrigin, L.A., 1963. The convergence of the random search method in the extremal control of a many parameter system. Automaton & Remote Control, 24, pp.1337-1342. https://tinyurl.com/djfdnpx4

Brooks, S.H., 1958. A discussion of random methods for seeking maxima. Operations Research, 6(2), pp.244-251. https://pubsonline.informs.org/doi/abs/10.1287/opre.6.2.244

Ashby, W.R., 1952. Design for a brain: The origin of adaptive behaviour. Springer.

RSs