add spherical t design tutorial

Author: dietmarwo

README.adoc

@@ -117,8 +117,6 @@ separable CMA-ES and CMA-ES with Margin (see https://arxiv.org/abs/2205.13482) w
 parallel function evaluation.
 
 - Dual Annealing: Eigen based implementation in https://github.com/dietmarwo/fast-cma-es/blob/master/_fcmaescpp/daoptimizer.cpp[C++]. Use the https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.dual_annealing.html[scipy implementation] if you prefer a pure Python variant or need more configuration options. 
- 
-- GCL-DE: Eigen based implementation in https://github.com/dietmarwo/fast-cma-es/blob/master/_fcmaescpp/gcldeoptimizer.cpp[C++]. See "A case learning-based differential evolution algorithm for global optimization of interplanetary trajectory design, Mingcheng Zuo, Guangming Dai, Lei Peng, Maocai Wang, Zhengquan Liu", https://doi.org/10.1016/j.asoc.2020.106451[doi].
 
 - Expressions: There are two operators for constructing expressions over optimization algorithms: Sequence and random choice.
 Not only the single objective algorithms above, but also scipy and NLopt optimization methods and custom algorithms can be used for defining algorithm expressions.