> For the complete documentation index, see [llms.txt](https://albertlidesign.gitbook.io/ibeso/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://albertlidesign.gitbook.io/ibeso/tutorial/multi-solution-system.md).

# Topology Optimization

[**Topology optimization (TO)**](https://en.wikipedia.org/wiki/Topology_optimization) is a mathematical method that optimizes material layout within a given design space, for a given set of [loads](https://en.wikipedia.org/wiki/Structural_load), [boundary conditions](https://en.wikipedia.org/wiki/Boundary_conditions) and [constraints](https://en.wikipedia.org/wiki/Constraint_\(mathematics\)) with the goal of maximizing the performance of the system (wiki).

iBESO imports multi-solution methods, which allows users to create diverse optimal topologies simultaneously. The solutions can bring about rich design diversity for designers.

### Tutorial

The main interface is show in Fig. 1. It includes three parts: a iBESO LOGO, a side bar and four optimizers. The side bar has multiple useful functions (a detailed introduction can be found [here](/ibeso/gui/main-interface.md)).&#x20;

The four optimizers are used to display the topologically optimized results. The name of four optimizers are Solution A, Solution B, Solution C and Solution D, repecrtively. Generally, they use four different multi-solution algorithms. Users can change their algorithm in the [Options Panel](/ibeso/gui/options-panel.md). Moreover, the size of the opotimizers is adaptively adjusted according to the pre-defined resolution (changed in the [Options Panel](/ibeso/gui/options-panel.md)).

![Fig. 1. The main interface](/files/7IRUftKJ2LSlKp9aLW4I)

Click <img src="/files/0gdF1TZTTe2mTW0LwSrc" alt="" data-size="line"> can launch the four optimizers and obtain the corresponding results, as shown in Fig. 2. During the optimization process, users can click <img src="/files/1j0CLz9G8EKcJ1UVa9PV" alt="" data-size="line"> to pause the program or click <img src="/files/y3Y5qJcf0vdV9yvOEMkH" alt="" data-size="line"> to reset all optimizers. Note that the optimization process doesn't allow users to change parameters and settings. Thus, most of buttons are inactive.

![Fig. 2. Running the four optimizers](/files/ytDaXWrjRwjOfGEgUb3B)

Besides, users can change the optimization parameters in the [Parameter Panel](/ibeso/gui/parameter-panel.md), such as target volume, filter radius and resolution. For example, Fig. 3 shows another example with different resolution.

![Fig. 3. Cantiliver examples with a different resoltion](/files/gh2NkNcMeCdgfg5DvdAL)

### Default Parameters

iBESO defines a cantiliver beam as the default example. Its optimization parameters are shown in the table below.

| Parameter                                                        | Default value          | Function                                                                                                                                                                                                                                              |
| ---------------------------------------------------------------- | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Resolution                                                       | 64 x 40                | The number of elements in the width (64) and the height (40) directions                                                                                                                                                                               |
| Filter radius                                                    | 3                      |                                                                                                                                                                                                                                                       |
| Target volume                                                    | 0.5                    | The final volume of results                                                                                                                                                                                                                           |
| [Young's modulus](https://en.wikipedia.org/wiki/Young's_modulus) | 1                      | A mechanical property that measures the tensile or compressive [stiffness](https://en.wikipedia.org/wiki/Stiffness) of a [solid](https://en.wikipedia.org/wiki/Solid) material when the force is applied lengthwise.                                  |
| [Poisson's ratio](https://en.wikipedia.org/wiki/Poisson's_ratio) | 0.3                    | A measure of the [deformation](https://en.wikipedia.org/wiki/Deformation_\(engineering\)) (expansion or contraction) of a material in directions perpendicular to the specific direction of [loading](https://en.wikipedia.org/wiki/Structural_load). |
| Scoring weight                                                   | 1                      | Defines the weight of the [Scoring System](/ibeso/tutorial/scoring-system.md)                                                                                                                                                                         |
| Drawing weight                                                   | 0.3                    | Defines the weight of the [Drawing System](/ibeso/tutorial/drawing-system.md)                                                                                                                                                                         |
| Save path                                                        |                        |                                                                                                                                                                                                                                                       |
| Smoothing                                                        | True                   |                                                                                                                                                                                                                                                       |
| Minimum random value                                             | 75%                    |                                                                                                                                                                                                                                                       |
| Maximum random value                                             | 100%                   |                                                                                                                                                                                                                                                       |
| Random seed                                                      | 2                      |                                                                                                                                                                                                                                                       |
| Solution A Algorithms                                            | Original BESO          |                                                                                                                                                                                                                                                       |
| Solution B Algorithms                                            | Random Perturbation    |                                                                                                                                                                                                                                                       |
| Solution C Algorithms                                            | Random Initial Designs |                                                                                                                                                                                                                                                       |
| Solution D Algorithms                                            | Random Perturbation    |                                                                                                                                                                                                                                                       |
| Brush radius                                                     | 2                      |                                                                                                                                                                                                                                                       |
| Brush hardness                                                   | 50%                    |                                                                                                                                                                                                                                                       |
| Brush opacity                                                    | 100%                   |                                                                                                                                                                                                                                                       |
