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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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Structure of the operator class.
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fig7: Structure of the operator class.

Mentions: In order to get a general understanding of how the immune operator plugins work, the overall architecture of a typical operator class and its relationship to the other components in AITSO are illustrated in Figure 7. A typical operator class comprises four key parts, including description, parameter list, function repository, and the task execution center. For each operator class, the descriptive information provides the user with some essential information about the operator class, which can be identified and represented by the host program. The operator type information tags the execution stage (selection, mutation, replacement, etc.) of the operator class. Therefore, the host program can match the available operators for each algorithm step, according to the “Type” property of the operators. Developers can also provide the user with some useful hints by writing information into the “description text” when releasing their plugins.


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

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

Structure of the operator class.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: Structure of the operator class.
Mentions: In order to get a general understanding of how the immune operator plugins work, the overall architecture of a typical operator class and its relationship to the other components in AITSO are illustrated in Figure 7. A typical operator class comprises four key parts, including description, parameter list, function repository, and the task execution center. For each operator class, the descriptive information provides the user with some essential information about the operator class, which can be identified and represented by the host program. The operator type information tags the execution stage (selection, mutation, replacement, etc.) of the operator class. Therefore, the host program can match the available operators for each algorithm step, according to the “Type” property of the operators. Developers can also provide the user with some useful hints by writing information into the “description text” when releasing their plugins.

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