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AITSO: a tool for spatial optimization based on artificial immune systems.

Zhao X, Liu Y, Liu D, Ma X - Comput Intell Neurosci (2015)

Bottom Line: However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems.As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving.It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis.

View Article: PubMed Central - PubMed

Affiliation: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China.

ABSTRACT
A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis.

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The class schema diagram of “CSStepInfo” and “CSParameter.”
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fig4: The class schema diagram of “CSStepInfo” and “CSParameter.”

Mentions: The key technology for customizing the spatial optimization model is the design of the “CSStepInfo” class (see Figure 4). Each instance of CSStepInfo records the model steps information, including the execution order in the model's process, the operator class used for computation, function name, and the parameters' information gathered from the GUI. Therefore, the process of the model, as defined by the user, can be stored as an object array of the CSStepInfo in the host program. Once the algorithm is started, it can complete an iteration process by calling the “execute” member function of each step objects stored in the array.


AITSO: a tool for spatial optimization based on artificial immune systems.

Zhao X, Liu Y, Liu D, Ma X - Comput Intell Neurosci (2015)

The class schema diagram of “CSStepInfo” and “CSParameter.”
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4307625&req=5

fig4: The class schema diagram of “CSStepInfo” and “CSParameter.”
Mentions: The key technology for customizing the spatial optimization model is the design of the “CSStepInfo” class (see Figure 4). Each instance of CSStepInfo records the model steps information, including the execution order in the model's process, the operator class used for computation, function name, and the parameters' information gathered from the GUI. Therefore, the process of the model, as defined by the user, can be stored as an object array of the CSStepInfo in the host program. Once the algorithm is started, it can complete an iteration process by calling the “execute” member function of each step objects stored in the array.

Bottom Line: However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems.As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving.It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis.

View Article: PubMed Central - PubMed

Affiliation: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China.

ABSTRACT
A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis.

Show MeSH