Limits...
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.

Show MeSH
A C# code sample demonstrates how the common operator works.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4307625&req=5

alg1: A C# code sample demonstrates how the common operator works.

Mentions: The “function repository” usually comprises one or several functions implementing various immune strategies. It allows developers to package a group function into one operator class. For example, as stated in Section 3, the mutation rate is determined based on the affinity of each antibody: the higher the affinity, the smaller the mutation rate. This means that developers can design any formula to calculate the mutation rate, as long as the formation can implement the above principle, and package them into the same operator class. Nevertheless, all functions should be released together with their parameters and descriptive information, so that users can get a grasp of the methods to use, the functionality of the function, and can configure validating parameters for the functions. Algorithm 1 is a simple example of implementation of the mutation function. In this example, the mutation rate is calculated by using the following formula [37]:(2)F′abi=Fabi−min⁡Fabimax⁡Fabi−min⁡Fabi, i=1,2,…,Nc,Pm=exp⁡(−2∗F′(abi))t,where  F(abi)  is the affinity of an antibody, Nc is the number of antibodies in the new population, and F′(abi) is the normalized affinity of the antibody; Pm is the mutation rate of an antibody; 2 is the empirical value to control the decay; and t is the current number of iterations.


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

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

A C# code sample demonstrates how the common operator works.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

alg1: A C# code sample demonstrates how the common operator works.
Mentions: The “function repository” usually comprises one or several functions implementing various immune strategies. It allows developers to package a group function into one operator class. For example, as stated in Section 3, the mutation rate is determined based on the affinity of each antibody: the higher the affinity, the smaller the mutation rate. This means that developers can design any formula to calculate the mutation rate, as long as the formation can implement the above principle, and package them into the same operator class. Nevertheless, all functions should be released together with their parameters and descriptive information, so that users can get a grasp of the methods to use, the functionality of the function, and can configure validating parameters for the functions. Algorithm 1 is a simple example of implementation of the mutation function. In this example, the mutation rate is calculated by using the following formula [37]:(2)F′abi=Fabi−min⁡Fabimax⁡Fabi−min⁡Fabi, i=1,2,…,Nc,Pm=exp⁡(−2∗F′(abi))t,where  F(abi)  is the affinity of an antibody, Nc is the number of antibodies in the new population, and F′(abi) is the normalized affinity of the antibody; Pm is the mutation rate of an antibody; 2 is the empirical value to control the decay; and t is the current number of iterations.

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