## 1 Introduction  
The development of structural topology optimization has already been observed for decades; nevertheless, this approach still plays a leading role in the area of engineering design. Along with the original novel concepts applied in many research and engineering fields, a variety of approaches to topology generation have been proposed over the years. Many survey papers, e.g., refs. [1; 2; 3; 4], supported by the recently published papers of [PERSON] et al. [5] and [PERSON] and [PERSON] [6], provided a broad discussion on various issues and aspects of topology optimization. Moreover, solving contemporary design problems coming from industry requires the elaboration of novel methods and approaches, which stimulates continuous research progress in the development of efficient topology optimization algorithms. The natural consequence of this progress is that nowadays, commercial structural analysis software often also offers performing topology optimization. This easy access to topological design is very helpful for engineers but, on the other hand, the results produced by the black box systems are not necessarily the best to be found. It is definitely a step towards the engineering implementation of topological designs but working on efficient topology generators is still welcome. The present paper follows this trend, showing how to build an original efficient generator of structural topologies. Hence, it is built as a Cellular Automaton with original update rules. The rules utilize information gathered within cells' neighborhoods and are applied to all cells/elements. The motivation for building the algorithm came from the idea of utilizing the benefits of local compliances sorting. This is conducted on two levels: first, the monotonic function mapping local compliances distribution is defined on the global level based on their sorted values; next, on the local level, for each cell, the compliances are sorted within the cell neighborhood. Among them, the three largest absolute values are selected, and this is the basis on which to formulate Cellular Automata update rules. This technique is somewhat inspired by the grey wolf optimizer [7] strategy, wherein the process of updating design variables refers to the positions of the three best fitted wolves, namely alpha, beta and delta, wolves. It is proposed that we refer to the topology algorithm that benefits from the adaptation of sorted compliances optimization as TABASCO. The developed algorithm is a modified version of the flexible Cellular Automata model presented previously. The implemented extension, regarding local-level cell sorting, allows us to improve the resulting compliance values. The advantages of the algorithm, both from numerical and practical engineering points of view, as compared to the others developed within the field, art as follows: The algorithm works based on simple update rules; its numerical implementation is not complicated; it does not require gradient computations; it can easily be combined with professional structural analysis programs which allows engineering applications. Moreover, generated topologies are free from so-called grey elements, i.e., ones of intermediate densities, and due to the grey elements elimination, it is easier to transform obtained numerical solution to a CAD model and to prepare elements for manufacturing. One of the most important advantages of using the algorithm proposed in this paper is that the implementation of additional filtering techniques is not necessary. However, it is worth stressing that, in favor of the CA approach, the neighborhood remains the same independent of the mesh size, whereas in the case of the filtering technique, the neighborhood size has to be selected depending on the mesh size. The problem related to the above is the generation of topologies for a large number of elements. The OC method requires adjusting filtering parameters in order to obtain a solid topology. In the case of the CA-based algorithm, it is not necessary.