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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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Results of the testing environmental monitoring network optimization problem.
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fig11: Results of the testing environmental monitoring network optimization problem.

Mentions: The optimization problem designed for this case was the optimization of the air quality monitoring network of Wuhan, which is located in central China (coordinates: 30°35′N 114°17′E), with a population of over 10 million people (2011 census). As shown in Figure 11(a), the air quality monitoring network of Wuhan is composed of nine monitoring stations. The air quality monitoring network of this city has been in use for several years. However, following the rapid growth of the city, the monitoring area should also have been expanded. In this case study, we assumed that the existing stations remain unchanged, and some new stations need to be added to the existing monitoring network. To solve this problem, the study area was first divided into a grid system in which each grid cell (1 km × 1 km) is a candidate monitoring station. The grid cells which are located in the rivers or lakes were deleted from the candidate station set, and the number of candidate stations was 2529. Therefore, a spatial layout scheme of the new stations was encoded into an antibody in artificial immune system, and a gene of the antibody represents a feasible location of the new stations.


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

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

Results of the testing environmental monitoring network optimization problem.
© Copyright Policy
Related In: Results  -  Collection

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

fig11: Results of the testing environmental monitoring network optimization problem.
Mentions: The optimization problem designed for this case was the optimization of the air quality monitoring network of Wuhan, which is located in central China (coordinates: 30°35′N 114°17′E), with a population of over 10 million people (2011 census). As shown in Figure 11(a), the air quality monitoring network of Wuhan is composed of nine monitoring stations. The air quality monitoring network of this city has been in use for several years. However, following the rapid growth of the city, the monitoring area should also have been expanded. In this case study, we assumed that the existing stations remain unchanged, and some new stations need to be added to the existing monitoring network. To solve this problem, the study area was first divided into a grid system in which each grid cell (1 km × 1 km) is a candidate monitoring station. The grid cells which are located in the rivers or lakes were deleted from the candidate station set, and the number of candidate stations was 2529. Therefore, a spatial layout scheme of the new stations was encoded into an antibody in artificial immune system, and a gene of the antibody represents a feasible location of the new stations.

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