About Aitso

Aitso is a user friendly professional software for spatial optimization and based on artificial immune systems. It is capable of solving various optimization problems. It is developed based on the “plugins – platform” architecture by using C# language and the open-source GIS components DotSpatial. It has provided a series of standard Application Programming Interfaces (APIs), which would encourage the researchers to develop their own problem-specific application plugins to solving the practical problems or to implement some advanced immune operators into the platform to improve the performance of the algorithm.

System Requirements

Item Requirements

Operating Systems

Windows 7 or latter
(XP and 2000 are not surpported)
.Net Framework 4.0 or later
Memory/RAM 2G or higher recommended

Spatial optimization

The process, during which the spatial entity achieves the optimal status under certain constrains, is referred to as spatial optimization. Most practical environmental problem can be regarded as a typical spatial optimization process, such as spatial sampling optimization, environmental monitoring networks design, land use optimization and etc. Spatial optimization in environmental modeling generally involves various high-dimensional, non-linear, and complex relationships.

Artificial Immune System (AIS)

Artificial Immune System can be defined as intelligent and adaptive computational systems inspired by the theoretical immunology, principles and mechanisms in order to solve real-world problems(Dasgupta 1998; de Castro and Timmis 2003). In the past decade, the AIS has been rapid development, since most of the typical immune algorithms including Negative Selection Algorithm (NSA), Clonal Selection Algorithm(CSA), artificial immune network models(aiNET, etc.) and etc. were proposed around the turn of the 21st century(see more at http://www.artificial-immune-systems.org/algorithms.shtml ).

AIS has been advocated and proven to be promising in spatial optimization.

Who might use Aitso?

As a universal tool, we assume that there might be three types of users who will use Aitso:

  • The spatial optimization problem researchers. These users have rich experiences in modeling and solving optimization problems. However, they might not be familiar with immune algorithms. For these users, the only thing they have to do is to focus on modeling the specific problems and do not have to understand the exact immune mechanism of the algorithm.
  • The immune algorithm researchers. They are familiar with the immune algorithms and are good at improving the performance of the algorithms. These users can use the APIs provided by Aitso to develop advanced immune operators. Once a new operator is developed and integrated into Aitso, it can be used to solve any optimization problems.
  • The decision makers. This type of users might not have training in immune algorithm and modeling the spatial optimization problems. But they are interested in solving the practical spatial optimization problems by using the immune algorithms. For these users, they can solve their problems as following steps: First, select an application developed by the problem researchers towards their practical problems. Then, build their own immune algorithms by trying several combinations of operators released by the algorithm researchers. Finally, run the solver to optimize their solutions.



  • Basic GIS function(Powered by DotSpatial)
    • Displaying of spatial data.
    • Manipulating of spatial data.
    • Analyzing of spatial data.
    • …….
  • Artificial immune Algorithms
    • Clonal selection algorithm.
    • aiNET (Planning).
    • …….
  • Spatial optimization problems
    • Environmental monitoring networks design.
    • Traveling Salesman Problem.
    • land use optimization (Planning).
    • …….
  • Methods used for Multi-Objectives
    • Weighted-based approaches (Fixed weight, adaptive weight…).
    • Pareto dominant approaches (Planning).
    • ……
  • Application Programming Interfaces
    • “Plugin-Host” based architecture.
    • Use the interface “ICSOperator” to develop and share advanced immune operator plugins.
    • Use the abstract class “CSAntibody” and the interface “ICSOptimizationProblem” to develop and share problem- specific application plugins.
    • ……
  • parallel programming support (Planning)
    • Using the Parallel Framework (PFX) libraries in .NET Framework 4.0 to parallelize the immune algorithms.
    • ……

Last edited Aug 2, 2014 at 7:23 AM by coderxiang, version 12